21.13.5 Medical Applications -- Skin Cancer, Melanoma

APP for Monitoring Skin Spots,
2019.
WWW Link. Code, Skin Spots. 1906
he app allows you to: take and organise photos of spots; compare two images of a spot side by side; email those images to someone (eg. doctor). That's all!

Xu, L., Jackowski, M., Goshtasby, A., Roseman, D., Bines, S., Yu, C., Dhawan, A., Huntley, A.,
Segmentation skin cancer images,
IVC(17), No. 1, January 1999, pp. 65-74.
Elsevier DOI Code, Segmentation. Software describe here is available from:
HTML Version. BibRef 9901

Golston, J.E., Moss, R.H., Stoecker, W.V.,
Boundary detection in skin tumor images: An overall approach and a radial search algorithm,
PR(23), No. 11, 1990, pp. 1235-1247.
Elsevier DOI 0401
BibRef

Zhang, Z., Stoecker, W.V., Moss, R.H.,
Border detection on digitized skin tumor images,
MedImg(19), No. 11, November 2000, pp. 1128-1143.
IEEE Top Reference. 0110
BibRef

Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M., Kittler, H.,
Automated melanoma recognition,
MedImg(20), No. 3, March 2001, pp. 233-239.
IEEE Top Reference. 0110
BibRef

Xu, S.C.[Shu-Chang], Ye, X.Z.[Xiu-Zi], Wu, Y.[Yin], Giron, F.[Franck], Leveque, J.L.[Jean-Luc], Querleux, B.[Bernard],
Automatic skin decomposition based on single image,
CVIU(110), No. 1, April 2008, pp. 1-6.
Elsevier DOI 0804
Skin decomposition; Melanin; Hemoglobin; Image process BibRef

Gomez, D.D.[David Delgado], Carstensen, J.M.[Jens Michael], Ersbøll, B.K.[Bjarne K.],
Collecting highly reproducible images to support dermatological medical diagnosis,
IVC(24), No. 2, 1 February 2006, pp. 186-191.
Elsevier DOI 0604
Image acquisition; Camera calibration; Diffuse illumination; Principal component analysis; Independent component analysis; Psoriasis BibRef

Taur, J.S.[Jinshiuh S.], Lee, G.H.[Gwo-Her], Tao, C.W., Chen, C.C.[Chien-Chou], Yang, C.W.[Ching-Wen],
Segmentation of Psoriasis Vulgaris Images Using Multiresolution-Based Orthogonal Subspace Techniques,
SMC-B(36), No. 2, April 2006, pp. 390-402.
IEEE DOI 0604
BibRef

Tang, J.S.[Jin-Shan],
A multi-direction GVF snake for the segmentation of skin cancer images,
PR(42), No. 6, June 2009, pp. 1172-1179.
Elsevier DOI 0902
Skin tumor; Boundary extraction; Gradient vector flow; Active contour; Multidirection BibRef

Serrano, C.[Carmen], Acha, B.[Begona],
Pattern analysis of dermoscopic images based on Markov random fields,
PR(42), No. 6, June 2009, pp. 1052-1057.
Elsevier DOI 0902
Dermoscopic images; Pattern classification; Markov random field BibRef

Jiménez, A.[Amaya], Serrano, C.[Carmen], Acha, B.[Begoña],
Automatic Detection of Globules, Streaks and Pigment Network Based on Texture and Color Analysis in Dermoscopic Images,
ICIAR17(486-493).
Springer DOI 1706
BibRef

Mendoza, C.S.[Carlos S.], Serrano, C.[Carmen], Acha, B.[Begoña],
Pattern Analysis of Dermoscopic Images Based on FSCM Color Markov Random Fields,
ACIVS09(676-685).
Springer DOI 0909
BibRef

Kallel, I.F.[Imen Fourati], Bouhlel, M.S.[Mohamed Salim], Lapayre, J.C.[Jean-Christophe], Garcia, E.[Eric],
Control of dermatology image integrity using reversible watermarking,
IJIST(19), No. 1, March 2009, pp. 5-9.
DOI Link 0902
BibRef

Peserico, E.[Enoch], Silletti, A.[Alberto],
Is (N)PRI suitable for evaluating automated segmentation of cutaneous lesions?,
PRL(31), No. 16, December 2010, pp. 2464-2467.
Elsevier DOI 1011
Segmentation; Evaluation; NPRI; Melanoma BibRef

La Torre, E.[Elisabetta], Caputo, B.[Barbara], Tommasi, T.[Tatiana],
Learning methods for melanoma recognition,
IJIST(20), No. 4, December 2010, pp. 316-322.
DOI Link 1011
BibRef
Earlier: A3, A1, A2:
Melanoma Recognition Using Representative and Discriminative Kernel Classifiers,
CVAMIA06(1-12).
Springer DOI 0605

See also Discriminative cue integration for medical image annotation. BibRef

Capdehourat, G.[Germán], Corez, A.[Andrés], Bazzano, A.[Anabella], Alonso, R.[Rodrigo], Musé, P.[Pablo],
Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions,
PRL(32), No. 16, 1 December 2011, pp. 2187-2196.
Elsevier DOI 1112
Melanoma; Dermoscopy; Pigmented skin lesion classification; Adaptive boosting; Support vector machines; Decision trees BibRef

Khan, R.[Rehanullah], Hanbury, A.[Allan], Stöttinger, J.[Julian], Bais, A.[Abdul],
Color based skin classification,
PRL(33), No. 2, 15 January 2012, pp. 157-163.
Elsevier DOI 1112
Skin detection; Skin classification; Color spaces and skin detection; Color constancy BibRef

Silva, C.S.P.[Cátia S. P.], Marcal, A.R.S.[André R. S.], Pereira, M.A.[Marta A.], Mendonça, T.[Teresa], Rozeira, J.[Jorge],
Separability Analysis of Color Classes on Dermoscopic Images,
ICIAR12(II: 268-277).
Springer DOI 1206
BibRef

Zhou, Y.[Yu], Smith, M.L.[Melvyn L.], Smith, L.N.[Lyndon N.], Warr, R.[Robert],
Combinatorial photometric stereo and its application in 3D modeling of melanoma,
MVA(23), No. 5, September 2012, pp. 1029-1045.
WWW Link. 1208
BibRef

Ma, L.[Li], Staunton, R.C.[Richard C.],
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition,
PR(46), No. 1, January 2013, pp. 98-106.
Elsevier DOI 1209
Melanoma detection; Structural irregularity of contours; Wavelet decomposition; Multi-scale descriptors; Significant wavelet sub-bands BibRef

Abbas, Q.[Qaisar], Celebi, M.E., Serrano, C.[Carmen], García, I.F.[Irene Fondón], Ma, G.[Guangzhi],
Pattern classification of dermoscopy images: A perceptually uniform model,
PR(46), No. 1, January 2013, pp. 86-97.
Elsevier DOI 1209
Dermoscopy; Pattern classification; Steerable pyramid transform; Human visual system; AdaBoost; Multi-label learning BibRef

Lu, C.[Cheng], Mahmood, M.[Muhammad], Jha, N.[Naresh], Mandal, M.[Mrinal],
Detection of melanocytes in skin histopathological images using radial line scanning,
PR(46), No. 2, February 2013, pp. 509-518.
Elsevier DOI 1210
Histopathological image analysis; Object detection; Image analysis; Melanocytes BibRef

Lu, C.[Cheng], Mandal, M.[Mrinal],
Automated analysis and diagnosis of skin melanoma on whole slide histopathological images,
PR(48), No. 8, 2015, pp. 2738-2750.
Elsevier DOI 1505
Histopathological image analysis BibRef

d'Alessandro, B., Dhawan, A.P.,
3-D Volume Reconstruction of Skin Lesions for Melanin and Blood Volume Estimation and Lesion Severity Analysis,
MedImg(31), No. 11, November 2012, pp. 2083-2092.
IEEE DOI 1211
BibRef

Smaoui, N.[Nadia], Bessassi, S.[Souhir],
A Developed System for Melanoma Diagnosis,
IJCVSP(3), No. 1, 2013, pp. xx-yy.
WWW Link. 1309
BibRef

Scharcanski, J.[Jacob], Celebi, M.E.[M. Emre], (Eds.),
Computer Vision Techniques for the Diagnosis of Skin Cancer,

Saez, A., Serrano, C., Acha, B.,
Model-Based Classification Methods of Global Patterns in Dermoscopic Images,
MedImg(33), No. 5, May 2014, pp. 1137-1147.
IEEE DOI 1405
Classification BibRef

Serna, A.[Andrés], Marcotegui, B.[Beatriz], Decencière, E.[Etienne], Baldeweck, T.[Thérèse], Pena, A.M.[Ana-Maria], Brizion, S.[Sébastien],
Segmentation of elongated objects using attribute profiles and area stability: Application to melanocyte segmentation in engineered skin,
PRL(47), No. 1, 2014, pp. 172-182.
Elsevier DOI 1408
Mathematical morphology BibRef

Lu, Y., Xie, F., Wu, Y., Jiang, Z., Meng, R.,
No Reference Uneven Illumination Assessment for Dermoscopy Images,
SPLetters(22), No. 5, May 2015, pp. 534-538.
IEEE DOI 1411
Algorithm design and analysis BibRef

Schwarz, M., Omar, M., Buehler, A., Aguirre, J., Ntziachristos, V.,
Implications of Ultrasound Frequency in Optoacoustic Mesoscopy of the Skin,
MedImg(34), No. 2, February 2015, pp. 672-677.
IEEE DOI 1502
Dermis BibRef

Jiji, G.W.[Gnanasigamony Wiselin], Raj, P.S.J.D.[Peter Savariraj Johnson Durai],
Content-based image retrieval in dermatology using intelligent technique,
IET-IPR(9), No. 4, 2015, pp. 306-317.
DOI Link 1505
content-based retrieval BibRef

Ledoux, A.[Audrey], Richard, N.[Noël], Capelle-Laizé, A.S.[Anne-Sophie], Fernandez-Maloigne, C.[Christine],
Perceptual color hit-or-miss transform: Application to dermatological image processing,
SIViP(9), No. 5, July 2015, pp. 1081-1091.
Springer DOI 1506
BibRef

Xu, H.M.[Hong-Ming], Mandal, M.[Mrinal],
Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm,
JIVP(2015), No. 1, 2015, pp. 18.
DOI Link 1507
BibRef

Lu, C.[Cheng], Ma, Z.[Zhen], Mandal, M.,
Automated segmentation of the epidermis area in skin whole slide histopathological images,
IET-IPR(9), No. 9, 2015, pp. 735-742.
DOI Link 1509
biomedical optical imaging BibRef

Kruk, M.[Michal], Swiderski, B.[Bartosz], Osowski, S.[Stanislaw], Kurek, J.[Jaroslaw], Slowinska, M.[Monika], Walecka, I.[Irena],
Melanoma recognition using extended set of descriptors and classifiers,
JIVP(2015), No. 1, 2015, pp. 43.
DOI Link 1601
BibRef

Sáez, A., Sánchez-Monedero, J., Gutiérrez, P.A., Hervás-Martínez, C.,
Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images,
MedImg(35), No. 4, April 2016, pp. 1036-1045.
IEEE DOI 1604
Color BibRef

Bae, J.S.[Ji-Sang], Jeon, J.H.[Jae-Ho], Lee, J.Y.[Jae-Young], Kim, J.O.[Jong-Ok],
Skin Condition Estimation Using Mobile Handheld Camera,
ETRI(38), No. 4, August 2016, pp. 776-786.
DOI Link 1608
BibRef

Noroozi, N.[Navid], Zakerolhosseini, A.[Ali],
Computer assisted diagnosis of basal cell carcinoma using Z-transform features,
JVCIR(40, Part A), No. 1, 2016, pp. 128-148.
Elsevier DOI 1609
Skin cancer BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.], Rozeira, J.[Jorge],
Clinically inspired analysis of dermoscopy images using a generative model,
CVIU(151), No. 1, 2016, pp. 124-137.
Elsevier DOI 1610
BibRef
Earlier: A1, A3, A2, Only:
Improving dermoscopy image analysis using color constancy,
ICIP14(3527-3531)
IEEE DOI 1502
Melanoma. Design automation BibRef

Ghanta, S., Jordan, M.I., Kose, K., Brooks, D.H., Rajadhyaksha, M., Dy, J.G.,
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin,
IP(26), No. 1, January 2017, pp. 172-184.
IEEE DOI 1612
Markov processes BibRef

Ferri, M.[Massimo], Tomba, I.[Ivan], Visotti, A.[Andrea], Stanganelli, I.[Ignazio],
A Feasibility Study for a Persistent Homology-Based k-Nearest Neighbor Search Algorithm in Melanoma Detection,
JMIV(57), No. 3, March 2017, pp. 324-339.
Springer DOI 1702
BibRef

Xie, F., Fan, H., Li, Y., Jiang, Z., Meng, R., Bovik, A.,
Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model,
MedImg(36), No. 3, March 2017, pp. 849-858.
IEEE DOI 1703
Cancer BibRef

Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.,
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks,
MedImg(36), No. 4, April 2017, pp. 994-1004.
IEEE DOI 1704
Biomedical imaging BibRef

Roy, A.[Anandarup], Pal, A.[Anabik], Garain, U.[Utpal],
JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation,
PR(66), No. 1, 2017, pp. 160-173.
Elsevier DOI 1704
BibRef
And: Erratum: PR(77), 2018, pp. 464.
Elsevier DOI 1802
Mixture model BibRef

Barata, C.[Catarina], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Development of a clinically oriented system for melanoma diagnosis,
PR(69), No. 1, 2017, pp. 270-285.
Elsevier DOI 1706
Melanoma, diagnosis BibRef

Ohki, K.[Keiichi], Celebi, M.E.[M. Emre], Schaefer, G.[Gerald], Iyatomi, H.[Hitoshi],
Building of Readable Decision Trees for Automated Melanoma Discrimination,
ISVC15(II: 712-721).
Springer DOI 1601
BibRef

Schwarz, M., Soliman, D., Omar, M., Buehler, A., Ovsepian, S.V., Aguirre, J., Ntziachristos, V.,
Optoacoustic Dermoscopy of the Human Skin: Tuning Excitation Energy for Optimal Detection Bandwidth With Fast and Deep Imaging in vivo,
MedImg(36), No. 6, June 2017, pp. 1287-1296.
IEEE DOI 1706
Attenuation, Bandwidth, Detectors, Frequency response, Phantoms, Skin, Angiographic imaging, evaluation and performance, image quality assessment, optimization, optoacoustic/photo-acoustic imaging, skin, tissue modelling, vessels, visualization BibRef

Bae, J.S.[Ji-Sang], Lee, S.H.[Sang-Ho], Choi, K.S.[Kang-Sun], Kim, J.O.[Jong-Ok],
Robust skin-roughness estimation based on co-occurrence matrix,
JVCIR(46), No. 1, 2017, pp. 13-22.
Elsevier DOI 1706
Skin, roughness BibRef

Sadri, A.R.[Amir Reza], Azarianpour, S.[Sepideh], Zekri, M.[Maryam], Celebi, M.E.[Mehmet Emre], Sadri, S.[Saeid],
WN-based approach to melanoma diagnosis from dermoscopy images,
IET-IPR(11), No. 7, July 2017, pp. 475-482.
DOI Link 1707
BibRef

Yuan, Y., Chao, M., Lo, Y.C.,
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance,
MedImg(36), No. 9, September 2017, pp. 1876-1886.
IEEE DOI 1709
cancer, endoscopes, neural nets, skin, tumours, Jaccard distance, automatic skin lesion segmentation, melanoma detection challenge, Malignant tumors, BibRef

Adjed, F.[Faouzi], Gardezi, S.J.S.[Syed Jamal Safdar], Ababsa, F.[Fakhreddine], Faye, I.[Ibrahima], Dass, S.C.[Sarat Chandra],
Fusion of structural and textural features for melanoma recognition,
IET-CV(12), No. 2, March 2018, pp. 185-195.
DOI Link 1804
BibRef

Bi, L.[Lei], Kim, J.M.[Jin-Man], Ahn, E.[Euijoon], Kumar, A.[Ashnil], Feng, D.D.[David Dagan], Fulham, M.[Michael],
Step-wise integration of deep class-specific learning for dermoscopic image segmentation,
PR(85), 2019, pp. 78-89.
Elsevier DOI 1810
Dermoscopic, Melanoma, Segmentation, Fully convolutional networks (FCN) BibRef

Ahn, E.[Euijoon], Kumar, A.[Ashnil], Fulham, M.[Michael], Feng, D.D.[David Dagan], Kim, J.M.[Jin-Man],
Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation,
MedImg(39), No. 7, July 2020, pp. 2385-2394.
IEEE DOI 2007
Biomedical imaging, Feature extraction, Training, Skin, Diseases, Training data, Convolutional auto-encoders, unsupervised feature learning BibRef

Do, T., Hoang, T., Pomponiu, V., Zhou, Y., Chen, Z., Cheung, N., Koh, D., Tan, A., Tan, S.,
Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis,
MultMed(20), No. 10, October 2018, pp. 2849-2864.
IEEE DOI 1810
Lesions, Skin, Malignant tumors, Feature extraction, Smart phones, Image color analysis, Image analysis, human-computer interface BibRef

Xia, Y., Zhang, L., Meng, L., Yan, Y., Nie, L., Li, X.,
Exploring Web Images to Enhance Skin Disease Analysis Under A Computer Vision Framework,
Cyber(48), No. 11, November 2018, pp. 3080-3091.
IEEE DOI 1810
Skin, Diseases, Visualization, Training, Hospitals, Feature extraction, Skin disease inference, transfer learning BibRef

Sultana, N.N.[Nazneen N.], Mandal, B.[Bappaditya], Puhan, N.B.,
Deep residual network with regularised fisher framework for detection of melanoma,
IET-CV(12), No. 8, December 2018, pp. 1096-1104.
DOI Link 1812
BibRef

Nguyen, K.L., Delachartre, P., Berthier, M.,
Multi-Grid Phase Field Skin Tumor Segmentation in 3D Ultrasound Images,
IP(28), No. 8, August 2019, pp. 3678-3687.
IEEE DOI 1907
biomedical ultrasonics, image segmentation, medical image processing, nonparametric statistics, skin, tumours, exact solutions BibRef

Zhang, J., Xie, Y., Xia, Y., Shen, C.,
Attention Residual Learning for Skin Lesion Classification,
MedImg(38), No. 9, September 2019, pp. 2092-2103.
IEEE DOI 1909
Lesions, Skin, Melanoma, Learning systems, Task analysis, Visualization, Computational modeling, Attention learning, dermoscopy images BibRef

Mirbeik-Sabzevari, A., Oppelaar, E., Ashinoff, R., Tavassolian, N.,
High-Contrast, Low-Cost, 3-D Visualization of Skin Cancer Using Ultra-High-Resolution Millimeter-Wave Imaging,
MedImg(38), No. 9, September 2019, pp. 2188-2197.
IEEE DOI 1909
Imaging, Skin cancer, Surgery, Tumors, Millimeter wave technology, Skin, Millimeter-wave imaging, skin cancer imaging, malignant skin tissue BibRef

Sarkar, R.[Rahul], Chatterjee, C.C.[Chandra Churh], Hazra, A.[Animesh],
Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network,
IET-IPR(13), No. 12, October 2019, pp. 2130-2142.
DOI Link 1911
BibRef

Khan, M.A.[Muhammad Attique], Sharif, M.[Muhammad], Akram, T.[Tallha], Bukhari, S.A.C.[Syed Ahmad Chan], Nayak, R.S.[Ramesh Sunder],
Developed Newton-Raphson based deep features selection framework for skin lesion recognition,
PRL(129), 2020, pp. 293-303.
Elsevier DOI 2001
Skin cancer, Contrast stretching, Lesion localization, Deep features, Best features BibRef

Wang, X., Jiang, X., Ding, H., Liu, J.,
Bi-Directional Dermoscopic Feature Learning and Multi-Scale Consistent Decision Fusion for Skin Lesion Segmentation,
IP(29), 2020, pp. 3039-3051.
IEEE DOI 2002
Lesions, Skin, Image segmentation, Melanoma, Bidirectional control, Reliability, Feature extraction, Skin lesion segmentation, multi-scale consistent decision fusion BibRef

Amin, J.[Javeria], Sharif, A.[Abida], Gul, N.[Nadia], Anjum, M.A.[Muhammad Almas], Nisar, M.W.[Muhammad Wasif], Azam, F.[Faisal], Bukhari, S.A.C.[Syed Ahmad Chan],
Integrated design of deep features fusion for localization and classification of skin cancer,
PRL(131), 2020, pp. 63-70.
Elsevier DOI 2004
Cells, Principle component analysis (PCA), Melanoma, Alexnet, VGG-16 BibRef

Jayapriya, K.[Kalyanakumar], Jacob, I.J.[Israel Jeena],
Hybrid fully convolutional networks-based skin lesion segmentation and melanoma detection using deep feature,
IJIST(30), No. 2, 2020, pp. 348-357.
DOI Link 2005
deep residual network (DRN), hybrid fully convolutional networks, melanoma detection BibRef

Zhou, W., Chen, Z., Zhou, Q., Xing, D.,
Optical Biopsy of Melanoma and Basal Cell Carcinoma Progression by Noncontact Photoacoustic and Optical Coherence Tomography: In Vivo Multi-Parametric Characterizing Tumor Microenvironment,
MedImg(39), No. 6, June 2020, pp. 1967-1974.
IEEE DOI 2006
Photoacoustic imaging, OCT, skin, multi-modality fusion, integration of multiscale information BibRef

Phan, T.B.[Tan-Binh], Trinh, D.H.[Dinh-Hoan], Wolf, D.[Didier], Daul, C.[Christian],
Optical flow-based structure-from-motion for the reconstruction of epithelial surfaces,
PR(105), 2020, pp. 107391.
Elsevier DOI 2006
3D Image mosaicing, Structure-from-Motion (SfM), Dense optical flow, Endoscopy, Dermatology BibRef

Xiao, F.[Feng], Wu, Q.[Qiuxia],
Visual saliency based global-local feature representation for skin cancer classification,
IET-IPR(14), No. 10, August 2020, pp. 2140-2148.
DOI Link 2008
BibRef

Bi, L.[Lei], Feng, D.D.[David Dagan], Fulham, M.[Michael], Kim, J.M.[Jin-Man],
Multi-Label classification of multi-modality skin lesion via hyper-connected convolutional neural network,
PR(107), 2020, pp. 107502.
Elsevier DOI 2008
Classification, Melanoma, Convolutional neural networks (cnns) BibRef

Fu, X.H.[Xiao-Hang], Bi, L.[Lei], Kumar, A.[Ashnil], Fulham, M.[Michael], Kim, J.M.[Jin-Man],
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images,
MedImg(41), No. 11, November 2022, pp. 3266-3277.
IEEE DOI 2211
Lesions, Skin, Feature extraction, Visualization, Melanoma, Topology, Correlation, Classification, dermoscopy, melanoma, multimodal image BibRef

Divya, D., Ganeshbabu, T.R.,
Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection,
IJIST(30), No. 3, 2020, pp. 731-752.
DOI Link 2008
dermoscopic image, melanoma skin cancer, modified deer hunting algorithm, optimized region growing, recurrent neural network BibRef

Kalra, M.[Meeta], Bouguila, N.[Nizar], Fan, W.T.[Wen-Tao],
Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis,
IJIST(30), No. 3, 2020, pp. 794-814.
DOI Link 2008
biomedical images, brain tumor detection, CAD of malaria, colon cancer, diabetic retinopathy, feature extraction, skin melanoma BibRef

Genina, E.A., Surkov, Y.I., Serebryakova, I.A., Bashkatov, A.N., Tuchin, V.V., Zharov, V.P.,
Rapid Ultrasound Optical Clearing of Human Light and Dark Skin,
MedImg(39), No. 10, October 2020, pp. 3198-3206.
IEEE DOI 2010
Skin, Optical imaging, Biomedical optical imaging, Optical refraction, Optical variables control, sonophoresis BibRef

Wang, Q., Sun, L., Wang, Y., Zhou, M., Hu, M., Chen, J., Wen, Y., Li, Q.,
Identification of Melanoma From Hyperspectral Pathology Image Using 3D Convolutional Networks,
MedImg(40), No. 1, January 2021, pp. 218-227.
IEEE DOI 2012
Microscopy, segmentation, skin, quantification and estimation, optical imaging BibRef

Khan, M.A.[Muhammad Attique], Akram, T.[Tallha], Zhang, Y.D.[Yu-Dong], Sharif, M.[Muhammad],
Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework,
PRL(143), 2021, pp. 58-66.
Elsevier DOI 2102
Skin cancer, Mask RCNN, Transfer learning, Optimal features, ELM BibRef

Jiji, G.W.[G. Wiselin], Rajesh, A., Raj, P.J.D.[P. Johnson Durai],
CBI + R: A Fusion Approach to Assist Dermatological Diagnoses,
IJIG(21), No. 1 2021, pp. 2150005.
DOI Link 2102
BibRef

Zhang, B., Wang, Z., Gao, J., Rutjes, C., Nufer, K., Tao, D., Feng, D.D., Menzies, S.W.,
Short-Term Lesion Change Detection for Melanoma Screening With Novel Siamese Neural Network,
MedImg(40), No. 3, March 2021, pp. 840-851.
IEEE DOI 2103
Deep learning, Image segmentation, Neural networks, Melanoma, Feature extraction, Lesions, Task analysis, Melanoma screening, deep learning BibRef

Mishra, S.[Sourav], Chaudhury, S.[Subhajit], Imaizumi, H.[Hideaki], Yamasaki, T.[Toshihiko],
Robustness of Deep Learning Models in Dermatological Evaluation: A Critical Assessment,
IEICE(E104-D), No. 3, March 2021, pp. 419-429.
WWW Link. 2103
BibRef

Majji, R.[Ramachandro], Prakash, P.G.O.[Ponnusamy Gnanaprakasam Om], Cristin, R.[Rajan], Parthasarathy, G.[Govindaswamy],
Social bat optimisation dependent deep stacked auto-encoder for skin cancer detection,
IET-IPR(14), No. 16, 19 December 2020, pp. 4122-4131.
DOI Link 2103
BibRef

Dremin, V., Marcinkevics, Z., Zherebtsov, E., Popov, A., Grabovskis, A., Kronberga, H., Geldnere, K., Doronin, A., Meglinski, I., Bykov, A.,
Skin Complications of Diabetes Mellitus Revealed by Polarized Hyperspectral Imaging and Machine Learning,
MedImg(40), No. 4, April 2021, pp. 1207-1216.
IEEE DOI 2104
Skin, Diabetes, Hyperspectral imaging, Blood, Aging, Biological tissues, Medical diagnostic imaging, skin complications BibRef

Thiyaneswaran, B., Anguraj, K., Kumarganesh, S., Thangaraj, K.,
Early detection of melanoma images using gray level co-occurrence matrix features and machine learning techniques for effective clinical diagnosis,
IJIST(31), No. 2, 2021, pp. 682-694.
DOI Link 2105
COVID, FFBPNN, gamma correction, GLCM, HSI, K-means, SVM BibRef

Alizadeh, S.M.[Seyed Mohammad], Mahloojifar, A.[Ali],
Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features,
IJIST(31), No. 2, 2021, pp. 695-707.
DOI Link 2105
computer-aided diagnosis, convolutional neural networks, kernel principal component analysis, melanoma, skin cancer, texture feature BibRef

Gautam, A.[Anjali], Raman, B.[Balasubramanian],
Towards accurate classification of skin cancer from dermatology images,
IET-IPR(15), No. 9, 2021, pp. 1971-1986.
DOI Link 2106
BibRef

Ashour, A.S.[Amira S.], Wahba, M.A.[Maram A.], Alaa, E.E.[Eman Elsaid], Guo, Y.H.[Yan-Hui], Hawas, A.R.[Ahmed Refaat],
A novel diagnostic map for computer-aided diagnosis of skin cancer,
IET-IPR(15), No. 4, 2021, pp. 897-907.
DOI Link 2106
BibRef

Durgarao, N.[Nagayalanka], Sudhavani, G.[Ghanta],
Diagnosing skin cancer via C-means segmentation with enhanced fuzzy optimization,
IET-IPR(15), No. 10, 2021, pp. 2266-2280.
DOI Link 2108
BibRef

Pollastri, F.[Federico], Parreño, M.[Mario], Maroñas, J.[Juan], Bolelli, F.[Federico], Paredes, R.[Roberto], Ramos, D.[Daniel], Grana, C.[Costantino],
A deep analysis on high-resolution dermoscopic image classification,
IET-CV(15), No. 7, 2021, pp. 514-526.
DOI Link 2109
BibRef

Ding, B.J.[Bao-Jin], Wang, H.X.[Hai-Xia], Chen, P.[Peng], Zhang, Y.L.[Yi-Long], Liang, R.H.[Rong-Hua], Liu, Y.P.[Yi-Peng],
Subcutaneous sweat pore estimation from optical coherence tomography,
IET-IPR(15), No. 13, 2021, pp. 3267-3280.
DOI Link 2110
BibRef

Zhu, W.[Wei], Li, W.B.[Wen-Bin], Liao, H.F.[Hao-Fu], Luo, J.B.[Jie-Bo],
Temperature network for few-shot learning with distribution-aware large-margin metric,
PR(112), 2021, pp. 107797.
Elsevier DOI 2102
Few-shot learning, Metric learning, Skin lesion classification, Temperature function BibRef

Thepade, S.D.[Sudeep D.], Ramnani, G.[Gaurav], Mandhare, S.[Shubham],
Melanoma skin cancer identification with amalgamated TSBTC and BTC colour features using ensemble of machine learning algorithms,
IJCVR(11), No. 6, 2021, pp. 616-639.
DOI Link 2111
BibRef

Choudhary, P.[Priya], Singhai, J.[Jyoti], Yadav, J.S.,
Curvelet and fast marching method-based technique for efficient artifact detection and removal in dermoscopic images,
IJIST(31), No. 4, 2021, pp. 2334-2345.
DOI Link 2112
curvelet, fast marching method, Frangi vesselness, melanoma BibRef

Sharma, M.[Moolchand], Jain, B.[Bhanu], Kargeti, C.[Chetan], Gupta, V.[Vinayak], Gupta, D.[Deepak],
Detection and Diagnosis of Skin Diseases Using Residual Neural Networks (RESNET),
IJIG(21), No. 5 2021, pp. 2140002.
DOI Link 2201
BibRef

Rajput, G.[Gunjan], Agrawal, S.[Shashank], Raut, G.[Gopal], Vishvakarma, S.K.[Santosh Kumar],
An accurate and noninvasive skin cancer screening based on imaging technique,
IJIST(32), No. 1, 2022, pp. 354-368.
DOI Link 2201
convolutional neural network, deep learning, detection, HAM10000 dataset, skin cancer classification BibRef

Yu, Z.[Zhen], Nguyen, J.[Jennifer], Nguyen, T.D.[Toan D.], Kelly, J.[John], Mclean, C.[Catriona], Bonnington, P.[Paul], Zhang, L.[Lei], Mar, V.[Victoria], Ge, Z.Y.[Zong-Yuan],
Early Melanoma Diagnosis With Sequential Dermoscopic Images,
MedImg(41), No. 3, March 2022, pp. 633-646.
IEEE DOI 2203
Lesions, Melanoma, Skin, Convolutional neural networks, Feature extraction, Cancer, Image recognition, Lesion alignment, early melanoma diagnosis BibRef

Boulahia, S.Y.[Said Yacine], Benatia, M.A.[Mohamed Akram], Bouzar, A.[Abderrahmane],
Att2ResNet: A deep attention-based approach for melanoma skin cancer classification,
IJIST(32), No. 2, 2022, pp. 476-489.
DOI Link 2203
attention mechanism, classification, deep learning, melanoma, skin cancer BibRef

Jiao, X.F.[Xiong-Fei], Li, J.[Juan], Zhao, Z.L.[Zhi-Lei], Badami, B.[Benjamin],
An all-inclusive computer-aided melanoma diagnosis based on soft computing,
IJIST(32), No. 4, 2022, pp. 1294-1306.
DOI Link 2207
decision trees, feature selection, fuzzy C-means, image segmentation, melanoma, quantum fluid search optimization algorithm BibRef

Filali, Y.[Youssef], El Khoukhi, H.[Hasnae], Sabri, M.A.[My Abdelouahed], Aarab, A.[Abdellah],
Analysis and Classification of Skin Cancer Based on Deep Learning Approach,
ISCV22(1-6)
IEEE DOI 2208
Deep learning, Computer architecture, Melanoma, Skin, Real-time systems, Convolutional neural networks, Lesions, Melanoma and non-melanoma skin cancer BibRef

Gajera, H.K.[Himanshu K.], Zaveri, M.A.[Mukesh A.], Nayak, D.R.[Deepak Ranjan],
Patch-based local deep feature extraction for automated skin cancer classification,
IJIST(32), No. 5, 2022, pp. 1774-1788.
DOI Link 2209
CNN, deep features, feature fusion, FNN, KPCA, skin cancer BibRef

He, X.Y.[Xiao-Yu], Wang, Y.[Yong], Zhao, S.[Shuang], Chen, X.[Xiang],
Co-Attention Fusion Network for Multimodal Skin Cancer Diagnosis,
PR(133), 2023, pp. 108990.
Elsevier DOI 2210
Skin cancer diagnosis, Convolutional neural networks, Multimodal fusion, Attention mechanism BibRef

Nguyen, H.T.[Hai Thanh], Luong, H.H.[Huong Hoang], Phan, P.T.[Phat Tan], Nguyen, H.H.D.[Hung Huy Duc], Ly, D.[Duong], Phan, D.M.[Duc Minh], Do, T.T.[Tin Trung],
HS-UNET-ID: An approach for human skin classification integrating between UNET and improved dense convolutional network,
IJIST(32), No. 6, 2022, pp. 1832-1845.
DOI Link 2212
convolutional neural networks, human skin images, skin cancer diseases, skin classification BibRef

Nawaz, M.[Marriam], Nazir, T.[Tahira], Masood, M.[Momina], Ali, F.[Farooq], Khan, M.A.[Muhammad Attique], Tariq, U.[Usman], Sahar, N.[Naveera], Damaševicius, R.[Robertas],
Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network,
IJIST(32), No. 6, 2022, pp. 2137-2153.
DOI Link 2212
deep learning, DenseNet, dermoscopy, melanoma, skin moles, UNET BibRef

Hu, L.[Libing], Zhang, Y.C.[Yong-Chun], Chen, K.D.[Kai-Di], Mobayen, S.[Saleh],
A computer-aided melanoma detection using deep learning and an improved African vulture optimization algorithm,
IJIST(32), No. 6, 2022, pp. 2002-2016.
DOI Link 2212
computer-aided diagnosis, convolutional neural network, improved African vulture optimization algorithm, melanoma, SIIM-ISIC melanoma dataset BibRef

Nowara, E.[Ewa], Mcduff, D.[Daniel], Sabharwal, A.[Ashutosh], Veeraraghavan, A.[Ashok],
Seeing Beneath the Skin with Computational Photography,
CACM(65), No. 12, December 2022, pp. 90-100.
DOI Link 2212
BibRef

Zhou, H.Y.[Hong-Yu], Lu, C.[Chixiang], Wang, L.S.[Lian-Sheng], Yu, Y.Z.[Yi-Zhou],
GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis,
MedImg(41), No. 12, December 2022, pp. 3498-3508.
IEEE DOI 2212
Dermatology, Training, Self-supervised learning, Biomedical imaging, Task analysis, Image representation, self-supervised pre-training BibRef

Kumar, V.A.[V. Akash], Mishra, V.[Vijaya], Arora, M.[Monika],
Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach,
IJIG(22), No. 5 2022, pp. 2250041.
DOI Link 2212
BibRef

Zhang, D.[Dong], Yang, J.[Jing], Du, S.[Shaoyi], Han, H.C.[Hong-Cheng], Ge, Y.Y.[Yu-Yan], Zhu, L.F.[Long-Fei], Li, C.[Ce], Xu, M.[Meifeng], Zheng, N.N.[Nan-Ning],
Coarse-to-fine feature representation based on deformable partition attention for melanoma identification,
PR(136), 2023, pp. 109247.
Elsevier DOI 2301
Histopathological image, Melanoma identification, Deformable convolution, Attention mechanism, Deep learning BibRef

Parajuli, M.[Madan], Shaban, M.[Mohamed], Phung, T.L.[Thuy L.],
Automated differentiation of skin melanocytes from keratinocytes in high-resolution histopathology images using a weakly-supervised deep-learning framework,
IJIST(33), No. 1, 2023, pp. 262-275.
DOI Link 2301
artificial intelligence, deep-machine learning, digital pathology, keratinocytes, melanocytes BibRef

Ain, Q.U.[Qurrat Ul], Al-Sahaf, H.[Harith], Xue, B.[Bing], Zhang, M.J.[Meng-Jie],
Automatically Diagnosing Skin Cancers From Multimodality Images Using Two-Stage Genetic Programming,
Cyber(53), No. 5, May 2023, pp. 2727-2740.
IEEE DOI 2305
Feature extraction, Skin, Image color analysis, Lesions, Contracts, Wavelet analysis, Visualization, Feature construction, skin cancer images BibRef

Ding, Q.[Qi], Razmjooy, N.[Navid],
An optimal diagnosis system for melanoma dermoscopy images based on enhanced design of horse herd optimizer,
IJIST(33), No. 3, 2023, pp. 1092-1107.
DOI Link 2305
dermoscopy, diagnosis, enhanced horse herd optimization algorithm, SIIM-ISIC dataset BibRef

Gayatri, E.[Erapaneni], Aarthy, S.L.,
Challenges and Imperatives of Deep Learning Approaches for Detection of Melanoma: A Review,
IJIG(23), No. 3 2023, pp. 2240012.
DOI Link 2306
BibRef

Bian, X.F.[Xiao-Fei], Pan, H.W.[Hai-Wei], Zhang, K.[Kejia], Liu, P.[Peng], Chen, C.L.[Chun-Ling],
Malignant melanoma dermoscopy image classification method based on multi-modal medical features,
IET-IPR(17), No. 9, 2023, pp. 2611-2627.
DOI Link 2307
image classification, feature extraction, medical image processing BibRef

Pérez, E.[Eduardo], Reyes, Ó.[Óscar],
Performing Melanoma Diagnosis by an Effective Multi-view Convolutional Network Architecture,
IJCV(131), No. 1, January 2023, pp. 3094-3117.
Springer DOI 2310
BibRef

Goceri, E.[Evgin],
Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets,
IJIST(33), No. 5, 2023, pp. 1727-1744.
DOI Link 2310
deep learning, dermoscopy image, image augmentation, skin lesion, wavelet packet transform BibRef

Renith, G., Senthilselvi, A.,
An efficient skin cancer detection and classification using Improved Adaboost Aphid-Ant Mutualism model,
IJIST(33), No. 6, 2023, pp. 1957-1972.
DOI Link 2311
AlexNet architecture, Aphid-Ant Mutualism, detection, Improved Adaboost, pre-processing, skin cancer BibRef

Söylemez, Ö.F.[Ömer Faruk],
Forward selection-based ensemble of deep neural networks for melanoma classification in dermoscopy images,
IJIST(33), No. 6, 2023, pp. 1929-1943.
DOI Link 2311
deep learning, ensembling, forward selection, melanoma, skin cancer BibRef

Sukanya, S.T., Jerine, S.,
Deep Learning-Based Melanoma Detection with Optimized Features via Hybrid Algorithm,
IJIG(23), No. 6 2023, pp. 2350056.
DOI Link 2312
BibRef

Vidhyalakshmi, A.M., Kanchana, M.,
Skin cancer classification using improved transfer learning model-based random forest classifier and golden search optimization,
IJIST(34), No. 1, 2024, pp. e22971.
DOI Link 2401
convolutional neural network (CNN), Golden search optimization (GSO) algorithm, transfer learning (TL) BibRef

Kaur, K.[Komalpreet], Kaur, A.[Amanpreet],
In silico, in vitro, and in vivo validation of a microwave imaging system using a low-profile Ultra Wide Band Archimedean spiral antenna to detect skin cancer,
IJIST(34), No. 1, 2024, pp. e23016.
DOI Link 2401
beamforming algorithm, in vitro, in vivo, microwave imaging, skin cancer, UWB ASMA BibRef

Enturi, B.K.M.[B. Krishna Manash], Suhasini, A., Satyala, N.[Narayana],
Optimized Deep CNN with Deviation Relevance-based LBP for Skin Cancer Detection: Hybrid Metaheuristic Enabled Feature Selection,
IJIG(24), No. 2, March 2024, pp. 2450023.
DOI Link 2404
BibRef

Ahmed, N.[Noor], Xin, T.[Tan], Lizhuang, M.[Ma],
MSPAN: Multi-scale pyramid attention network for efficient skin cancer lesion segmentation,
IET-IPR(18), No. 7, 2024, pp. 1667-1680.
DOI Link 2405
biomedical imaging, cancer, computer vision, convolution, convolutional neural nets BibRef

Tang, P.[Peng], Yan, X.T.[Xin-Tong], Nan, Y.[Yang], Hu, X.B.[Xia-Bin], Menze, B.H.[Bjoern H.], Krammer, S.[Sebastian], Lasser, T.[Tobias],
Joint-individual fusion structure with fusion attention module for multi-modal skin cancer classification,
PR(154), 2024, pp. 110604.
Elsevier DOI 2406
Skin cancer classification, Joint-individual fusion structure, Multi-modal fusion attention, Dermatological image and metadata BibRef

Imran, M.[Muhammad], Akram, M.U.[Muhammad Usman], Tiwana, M.I.[Mohsin Islam], Salam, A.A.[Anum Abdul], Hassan, T.[Taimur], Greco, D.[Danilo],
Two-dimensional hybrid incremental learning (2DHIL) framework for semantic segmentation of skin tissues,
IVC(148), 2024, pp. 105147.
Elsevier DOI 2407
BibRef
Earlier: A1, A2, A3, A4, A6, Only: IVC(148), 2024, pp. 105098. 2407
BibRef
And: Erratum: IVC(148), 2024, pp. 105148.
Elsevier DOI 2407
Note -- erratum seems to partly correct author list in first version. AI, Incremental semantic segmentation, Incremental learning, Continual learning, Knowledge distillation, Computational histopathology BibRef

Li, F.[Feng], Li, M.[Min], Zuo, E.[Enguang], Chen, C.[Chen], Chen, C.[Cheng], Lv, X.Y.[Xiao-Yi],
Self-contrastive Feature Guidance Based Multidimensional Collaborative Network of metadata and image features for skin disease classification,
PR(156), 2024, pp. 110742.
Elsevier DOI 2408
Multimodal fusion, Multidimensional synergy, Contrast learning, Feature refinement BibRef

Zhao, G.Z.[Guang-Zhe], Zhang, C.[Chen], Wang, X.P.[Xue-Ping], Lin, B.[Benwang], Yan, F.H.[Fei-Hu],
PMANet: Progressive multi-stage attention networks for skin disease classification,
IVC(149), 2024, pp. 105166.
Elsevier DOI 2408
Skin disease classification, Progressive networks, Attention mechanism BibRef

Yan, S.Y.[Si-Yuan], Yu, Z.[Zhen], Zhang, X.[Xuelin], Mahapatra, D.[Dwarikanath], Chandra, S.S.[Shekhar S.], Janda, M.[Monika], Soyer, P.[Peter], Ge, Z.Y.[Zong-Yuan],
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision,
CVPR23(11568-11577)
IEEE DOI 2309
BibRef

Grullon, S.[Sean], Spurrier, V.[Vaughn], Zhao, J.Y.[Jia-Yi], Chivers, C.[Corey], Jiang, Y.[Yang], Motaparthi, K.[Kiran], Lee, J.[Jason], Bonham, M.[Michael], Ianni, J.[Julianna],
Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression,
MIA-COVID19D22(442-456).
Springer DOI 2304
BibRef

Jaworek-Korjakowska, J.[Joanna], Wojcicka, A.[Anna], Kucharski, D.[Dariusz], Brodzicki, A.[Andrzej], Kendrick, C.[Connah], Cassidy, B.[Bill], Yap, M.H.[Moi Hoon],
Skin_hair Dataset: Setting the Benchmark for Effective Hair Inpainting Methods for Improving the Image Quality of Dermoscopic Images,
ISIC22(167-184).
Springer DOI 2304
BibRef

Du, S.[Siyi], Hers, B.[Ben], Bayasi, N.[Nourhan], Hamarneh, G.[Ghassan], Garbi, R.[Rafeef],
Fairdisco: Fairer AI in Dermatology via Disentanglement Contrastive Learning,
ISIC22(185-202).
Springer DOI 2304
BibRef

Li, L.F.[Ling-Fang], Qi, X.Y.[Xue-Ying], Wang, X.[Xu], Hu, W.J.[Wei-Jian], Du, Y.X.[Yong-Xing], Li, B.S.[Bao-Shan],
WAMF: A Weighted Adaptive Multi-convolution Fusion Scheme for Dermoscopic Image Recognition,
ICIVC22(561-572)
IEEE DOI 2301
Training, Adaptation models, Image recognition, Adaptive systems, Computational modeling, Transfer learning, image recognition BibRef

del Amor, R.[Rocío], Colomer, A.[Adrián], Morales, S.[Sandra], Pulgarín-Ospina, C.[Cristian], Terradez, L.[Liria], Aneiros-Fernandez, J.[Jose], Naranjo, V.[Valery],
A Self-Contrastive Learning Framework for Skin Cancer Detection Using Histological Images,
ICIP22(2291-2295)
IEEE DOI 2211
Pathology, Databases, Annotations, Manuals, Feature extraction, Artificial intelligence, Neoplasms, Self-training, Digital pathology BibRef

Göçeri, E.[Evgin],
Impact of Deep Learning and Smartphone Technologies in Dermatology: Automated Diagnosis,
IPTA20(1-6)
IEEE DOI 2206
Learning systems, Image processing, Dermatology, Skin, Lesions, Medical diagnostic imaging, Diseases, automated diagnosis, skin diseases BibRef

Casado-García, A.[Angela], Agirresarobe, A.[Aitor], Miranda-Apodaca, J.[Jon], Heras, J.[Jónathan], Pérez-López, U.[Usue],
Deep Detection Models for Measuring Epidermal Bladder Cells,
IbPRIA22(131-142).
Springer DOI 2205
BibRef

Alves, B.[Beatriz], Barata, C.[Catarina], Marques, J.S.[Jorge S.],
Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata,
IbPRIA22(69-80).
Springer DOI 2205
BibRef

Nau, M.A.[Merlin A.], Schiffers, F.[Florian], Li, Y.H.[Yun-Hao], Xu, B.J.[Bing-Jie], Maier, A.[Andreas], Tumblin, J.[Jack], Walton, M.[Marc], Katsaggelos, A.K.[Aggelos K.], Willomitzer, F.[Florian], Cossairt, O.[Oliver],
Skinscan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation,
ICIP21(2918-2922)
IEEE DOI 2201
Photography, Image processing, Skin, Software, Mobile handsets, Topographic Imaging, Three-Dimensional Imaging, Dermatologic Imaging BibRef

Alche, M.N.[Miguel Nehmad], Acevedo, D.[Daniel], Mejail, M.[Marta],
EfficientARL: improving skin cancer diagnoses by combining lightweight attention on EfficientNet,
CVAMD21(3347-3353)
IEEE DOI 2112
Image color analysis, Malignant tumors, Computational modeling, Melanoma, Skin BibRef

Rao, A.[Adrit], Park, J.[Jongchan], Woo, S.[Sanghyun], Lee, J.Y.[Joon-Young], Aalami, O.[Oliver],
Studying the Effects of Self-Attention for Medical Image Analysis,
CVAMD21(3409-3418)
IEEE DOI 2112
Visualization, Image analysis, Melanoma, Medical services, Market research, Convolutional neural networks BibRef

Sankarapandian, S.[Sivaramakrishnan], Kohn, S.[Saul], Spurrier, V.[Vaughn], Grullon, S.[Sean], Soans, R.E.[Rajath E.], Ayyagari, K.D.[Kameswari D.], Chamarthi, R.V.[Ramachandra V.], Motaparthi, K.[Kiran], Lee, J.B.[Jason B.], Shon, W.W.[Won-Woo], Bonham, M.[Michael], Ianni, J.D.[Julianna D.],
A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth *,
CDPath21(629-638)
IEEE DOI 2112
Deep learning, Pathology, Sensitivity, Melanoma, Tools, Routing BibRef

Stieler, F.[Fabian], Rabe, F.[Fabian], Bauer, B.[Bernhard],
Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach,
ISIC21(1802-1809)
IEEE DOI 2109
Deep learning, Image analysis, Computational modeling, Skin, Pattern recognition BibRef

Frasca, M.[Maria], Nappi, M.[Michele], Risi, M.[Michele], Tortora, G.[Genoveffa], Citarella, A.A.[Alessia Auriemma],
A Comparison of Neural Network Approaches for Melanoma Classification,
ICPR21(2110-2117)
IEEE DOI 2105
Deep learning, Sensitivity, Neural networks, Melanoma, Medical services, Manuals, Sensitivity and specificity BibRef

Xue, X.[Xi], Kamata, S.I.[Sei-Ichiro], Luo, D.M.[Da-Ming],
Skin Lesion Classification Using Weakly-supervised Fine-grained Method,
ICPR21(9083-9090)
IEEE DOI 2105
Regulators, Neural networks, Melanoma, Feature extraction, Skin, Pattern recognition, Lesions BibRef

Mahbod, A.[Amirreza], Schaefer, G.[Gerald], Wang, C.L.[Chun-Liang], Ecker, R.[Rupert], Dorffner, G.[Georg], Ellinger, I.[Isabella],
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification,
ICPR21(4047-4053)
IEEE DOI 2105
Visualization, Image resolution, Transfer learning, Melanoma, Receivers, Skin, Classification algorithms, Dermatology, skin cancer, transfer learning BibRef

Gupta, K.[Krishnam], Jaiprasad, Krishnan, M.[Monu], Narayanan, A.[Ajit], Narayan, N.S.[Nikhil S.],
Dual Stream Network with Selective Optimization for Skin Disease Recognition in Consumer Grade Images,
ICPR21(5262-5269)
IEEE DOI 2105
Image segmentation, Lighting, Streaming media, Network architecture, Skin, Classification algorithms, dual stream deep networks BibRef

Grove, R., Green, R.,
Melanoma and Nevi Classification using Convolution Neural Networks,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Convolution, Neural networks, Focusing, Melanoma, Lesions, Testing, melanoma, ResNet50, identification, classification BibRef

Sabri, M.A., Filali, Y., El Khoukhi, H., Aarab, A.,
Skin Cancer Diagnosis Using an Improved Ensemble Machine Learning model,
ISCV20(1-5)
IEEE DOI 2011
cancer, feature extraction, image classification, learning (artificial intelligence), medical image processing, ensemble learning. BibRef

Bakkouri, I.[Ibtissam], Afdel, K.[Karim],
Dermonet: A Computer-aided Diagnosis System for Dermoscopic Disease Recognition,
ICISP20(170-177).
Springer DOI 2009
BibRef

Bagchi, S., Banerjee, A., Bathula, D.R.,
Learning A Meta-Ensemble Technique For Skin Lesion Classification And Novel Class Detection,
ISIC20(3221-3228)
IEEE DOI 2008
Stacking, Training, Skin, Task analysis, Lesions, Image color analysis, Melanoma BibRef

Coppola, D., Lee, H.K., Guan, C.,
Interpreting mechanisms of prediction for skin cancer diagnosis using multi-task learning,
ISIC20(3162-3171)
IEEE DOI 2008
Task analysis, Lesions, Logic gates, Skin, Machine learning, Melanoma BibRef

Singh, N., Lee, K., Coz, D., Angermueller, C., Huang, S., Loh, A., Liu, Y.,
Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease Classification,
ISIC20(3172-3181)
IEEE DOI 2008
Skin, Diseases, Google, Analytical models, Dermatology, Data models, Image segmentation BibRef

Pacheco, A.G.C., Sastry, C.S., Trappenberg, T., Oore, S., Krohling, R.A.,
On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers,
ISIC20(3152-3161)
IEEE DOI 2008
Training, Melanoma, Neural networks, Detection algorithms, Dogs, Skin BibRef

Adegun, A.[Adekanmi], Viriri, S.[Serestina],
Deep Convolutional Network-based Framework for Melanoma Lesion Detection and Segmentation,
ACIVS20(51-62).
Springer DOI 2003
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Dogvanich, A., Mamaev, N., Krylov, A., Makhneva, N.,
Dermatological Image Denoising Using Adaptive Henlm Method,
PTVSBB19(47-52).
DOI Link 1912
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Peng, J., Gao, R., Nguyen, L., Liang, Y., Thng, S., Lin, Z.,
Classification of Non-Tumorous Facial Pigmentation Disorders Using Improved Smote and Transfer Learning,
ICIP19(220-224)
IEEE DOI 1910
improved SMOTE, facial pigmentation disorders, biomedical images analysis and classification, transfer learning BibRef

Neher, H.[Helmut], Arlette, J.[John], Wong, A.[Alexander],
Discovery Radiomics for Detection of Severely Atypical Melanocytic Lesions (SAML) from Skin Imaging via Deep Residual Group Convolutional Radiomic Sequencer,
ICIAR19(II:307-315).
Springer DOI 1909
BibRef

Salih, O.[Omran], Viriri, S.[Serestina],
Skin Cancer Segmentation Using a Unified Markov Random Field,
ISVC18(25-33).
Springer DOI 1811
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Elmogy, M.[Mohammed], García-Zapirain, B.[Begoña], Elmaghraby, A.S.[Adel S.], Ei-Baz, A.[Ayman],
An Automated Classification Framework for Pressure Ulcer Tissues Based on 3D Convolutional Neural Network,
ICPR18(2356-2361)
IEEE DOI 1812
Image segmentation, Image color analysis, Solid modeling, Skin, Kernel, Feature extraction BibRef

Elmogy, M.[Mohammed], García-Zapirain, B.[Begoña], Burns, C.[Connor], Elmaghraby, A.S.[Adel S.], Ei-Baz, A.[Ayman],
Tissues Classification for Pressure Ulcer Images Based on 3D Convolutional Neural Network,
ICIP18(3139-3143)
IEEE DOI 1809
Image segmentation, Kernel, Image color analysis, Biological system modeling, Solid modeling, Linear Combinations of Discrete Gaussians (LCDG) BibRef

Coronado, R.[Ricardo], Ocsa, A.[Alexander], Quispe, O.[Oscar],
Non-dermatoscopic Image Analysis for the Recognition of Malignant Skin Diseases with Convolutional Neural Network and Autoencoders,
CIARP17(160-167).
Springer DOI 1802
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Gu, Y., Zhou, J., Qian, B.,
Melanoma Detection Based on Mahalanobis Distance Learning and Constrained Graph Regularized Nonnegative Matrix Factorization,
WACV17(797-805)
IEEE DOI 1609
Feature extraction, Linear programming, Malignant tumors, Manifolds, Matrix decomposition, Skin, Skin, cancer BibRef

Barata, C.[Catarina], Figueiredo, M.A.T.[Mário A. T.], Celebi, M.E.[M. Emre], Marques, J.S.[Jorge S.],
Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding,
IbPRIA17(528-536).
Springer DOI 1706
BibRef

Alarifi, J.S.[Jhan S.], Goyal, M.[Manu], Davison, A.K.[Adrian K.], Dancey, D.[Darren], Khan, R.[Rabia], Yap, M.H.[Moi Hoon],
Facial Skin Classification Using Convolutional Neural Networks,
ICIAR17(479-485).
Springer DOI 1706
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Cho, D.S.[Daniel S.], Khalvati, F.[Farzad], Clausi, D.A.[David A.], Wong, A.[Alexander],
A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer,
ICIAR17(79-86).
Springer DOI 1706
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Yao, T.T.[Ting-Ting], Wang, Z.Y.[Zhi-Yong], Xie, Z.[Zhao], Gao, J.[Jun], Feng, D.D.[David Dagan],
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ICIP16(2633-2637)
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Cancer BibRef

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IPRIA15(1-6)
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cancer BibRef

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DICTA15(1-8)
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feature extraction BibRef

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ICIP15(4357-4360)
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ICCVG14(347-355).
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Liu, Z.[Zhao], Zerubia, J.B.[Josiane B.],
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Abuzaghleh, O., Barkana, B.D., Faezipour, M.,
SKINcure: A real time image analysis system to aid in the malignant melanoma prevention and early detection,
Southwest14(85-88)
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Barata, C.[Catarina], Marques, J.S.[Jorge S.], Mendonça, T.[Teresa],
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Barata, C.[Catarina], Marques, J.S.[Jorge S.], Rozeira, J.[Jorge],
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The Role of Keypoint Sampling on the Classification of Melanomas in Dermoscopy Images Using Bag-of-Features,
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Mete, M.[Mutlu], Ou, Y.L.[Ye-Lin], Sirakov, N.M.[Nikolay Metodiev],
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Hemoglobin and Melanin Quantification on Skin Images,
ICIAR12(II: 198-205).
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Diseases BibRef

Prigent, S.[Sylvain], Descombes, X.[Xavier], Zugaj, D.[Didier], Martel, P.[Philippe], Zerubia, J.B.[Josiane B.],
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Schaefer, G., Krawczyk, B., Celebi, M.E., Iyatomi, H.,
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ACPR13(386-390)
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biomedical optical imaging BibRef

Bouhlel, N.[Nizar], Hajjaji, S.[Salwa], Sevestre, S.[Sylvie], Laugier, P.[Pascal],
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Chaudhry, M.A.[M. Ali], Ashraf, R.[Robina], Jafri, M.N., Akbar, M.,
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MMBIA07(1-8).
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Poultry Skin Tumor Detection in Hyperspectral Reflectance Images by Combining Classifiers,
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Yamada, T., Saito, H., Ozawa, S.,
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Registration of NEVI in Successive Skin Images for Early Detection of Melanoma,
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Kutics, A.[Andrea], Date, M.[Munehiro],
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Skin Lesions, Wound Healing .