21.13.3 Medical Applications -- Prostate Cancer Analysis

Zhang, J., Liu, M., Shen, D.,
Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks,
IP(26), No. 10, October 2017, pp. 4753-4764.
IEEE DOI 1708
biomedical MRI, computerised tomography, feedforward neural nets, learning (artificial intelligence), medical image processing, object detection, regression analysis, 3D T1-weighted magnetic resonance images, 3D computed tomography images, CNN based regression model, Biological neural networks, Biomedical imaging, Machine learning, BibRef

Wang, Z., Liu, C., Cheng, D., Wang, L., Yang, X., Cheng, K.T.,
Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network,
MedImg(37), No. 5, May 2018, pp. 1127-1139.
IEEE DOI 1805
Cancer, Feature extraction, Lesions, Manuals, Neural networks, Principal component analysis, CS PCa detection, neural network BibRef

Salimi, A.[Ahad], Pourmina, M.A.[Mohammad Ali], Moin, M.S.[Mohammad-Shahram],
Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach,
SIViP(12), No. 8, November 2018, pp. 1629-1637.
Springer DOI 1809
BibRef

Wildeboer, R.R., van Sloun, R.J.G., Schalk, S.G., Mannaerts, C.K., van der Linden, J.C., Huang, P., Wijkstra, H., Mischi, M.,
Convective-Dispersion Modeling in 3D Contrast-Ultrasound Imaging for the Localization of Prostate Cancer,
MedImg(37), No. 12, December 2018, pp. 2593-2602.
IEEE DOI 1812
Principal component analysis, Dispersion, Kinetic theory, Imaging, 3D BibRef

Azizi, S., Bayat, S., Yan, P., Tahmasebi, A., Kwak, J.T., Xu, S., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Mousavi, P., Abolmaesumi, P.,
Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound,
MedImg(37), No. 12, December 2018, pp. 2695-2703.
IEEE DOI 1812
Ultrasonic imaging, Biopsy, Logic gates, Recurrent neural networks, Principal component analysis, Prostate cancer, cancer detection BibRef

Moradi, H., Tang, S., Salcudean, S.E.,
Toward Intra-Operative Prostate Photoacoustic Imaging: Configuration Evaluation and Implementation Using the da Vinci Research Kit,
MedImg(38), No. 1, January 2019, pp. 57-68.
IEEE DOI 1901
Transducers, Robots, Surgery, Probes, Image reconstruction, Tomography, Photoacoustic tomography, prostate cancer, limited view BibRef

He, K., Cao, X., Shi, Y., Nie, D., Gao, Y., Shen, D.,
Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks,
MedImg(38), No. 2, February 2019, pp. 585-595.
IEEE DOI 1902
Image segmentation, Computed tomography, Shape, Bladder, Task analysis, Robustness, Image segmentation, neural networks, prostate cancer BibRef

Li, W., Li, J., Sarma, K.V., Ho, K.C., Shen, S., Knudsen, B.S., Gertych, A., Arnold, C.W.,
Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images,
MedImg(38), No. 4, April 2019, pp. 945-954.
IEEE DOI 1904
Feature extraction, Image segmentation, Biomedical imaging, Glands, Prostate cancer, Solid modeling, Computer-aided diagnosis (CAD), region-based convolutional neural networks (R-CNN) BibRef

Cao, R., Bajgiran, A.M.[A. Mohammadian], Mirak, S.A.[S. Afshari], Shakeri, S., Zhong, X., Enzmann, D., Raman, S., Sung, K.,
Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet,
MedImg(38), No. 11, November 2019, pp. 2496-2506.
IEEE DOI 1911
Lesions, Principal component analysis, Magnetic resonance imaging, Biopsy, Training, Encoding, convolutional neural network BibRef

Wang, Y., Dou, H., Hu, X., Zhu, L., Yang, X., Xu, M., Qin, J., Heng, P., Wang, T., Ni, D.,
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound,
MedImg(38), No. 12, December 2019, pp. 2768-2778.
IEEE DOI 1912
Image segmentation, Shape, Ultrasonic imaging, Biomedical imaging, transrectal ultrasound BibRef

Jia, H., Xia, Y., Song, Y., Zhang, D., Huang, H., Zhang, Y., Cai, W.,
3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images,
MedImg(39), No. 2, February 2020, pp. 447-457.
IEEE DOI 2002
Image segmentation, Spatial resolution, Decoding, Convolution, Glands, magnetic resonance imaging BibRef

Zhu, Q., Du, B., Yan, P.,
Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation,
MedImg(39), No. 3, March 2020, pp. 753-763.
IEEE DOI 2004
Image segmentation, Biomedical imaging, Feature extraction, Neural networks, Shape, Training data, Adaptive systems, boundary-weighted loss BibRef

Wang, S., Nie, D., Qu, L., Shao, Y., Lian, J., Wang, Q., Shen, D.,
CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation,
MedImg(39), No. 6, June 2020, pp. 2151-2162.
IEEE DOI 2006
Image segmentation, male pelvic organ, deep learning, incomplete annotation, CT BibRef

Liu, Q., Dou, Q., Yu, L., Heng, P.A.,
MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data,
MedImg(39), No. 9, September 2020, pp. 2713-2724.
IEEE DOI 2009
Magnetic resonance imaging, Image segmentation, Training, Robustness, Data models, Biomedical imaging, Task analysis, knowledge transfer BibRef

Shao, Y., Wang, J., Wodlinger, B., Salcudean, S.E.,
Improving Prostate Cancer (PCa) Classification Performance by Using Three-Player Minimax Game to Reduce Data Source Heterogeneity,
MedImg(39), No. 10, October 2020, pp. 3148-3158.
IEEE DOI 2010
Principal component analysis, Feature extraction, Biopsy, Cancer, Radio frequency, Data source heterogeneity, BibRef

Wang, S., Liu, M., Lian, J., Shen, D.,
Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy,
MedImg(40), No. 1, January 2021, pp. 310-320.
IEEE DOI 2012
Image segmentation, Computed tomography, Feature extraction, Bladder, Proposals, Image coding, Image segmentation, CT image BibRef

Rossi, A., Hosseinzadeh, M., Bianchini, M., Scarselli, F., Huisman, H.,
Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI,
MedImg(40), No. 3, March 2021, pp. 986-995.
IEEE DOI 2103
Feature extraction, Cancer, Magnetic resonance imaging, Neural networks, Lesions, Task analysis, Prostate cancer, lesion similarity BibRef

Shen, T.C.[Tian-Cheng], Yang, Y.[Yibo], Lin, Z.C.[Zhou-Chen], Zhang, M.[Mingbin],
Recurrent learning with clique structures for prostate sparse-view CT artifacts reduction,
IET-IPR(15), No. 3, 2021, pp. 648-655.
DOI Link 2106
BibRef

Pinckaers, H.[Hans], Bulten, W.[Wouter], van der Laak, J.[Jeroen], Litjens, G.[Geert],
Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels,
MedImg(40), No. 7, July 2021, pp. 1817-1826.
IEEE DOI 2107
Training, Prostate cancer, Pathology, Biomedical imaging, Biological system modeling, Task analysis, Streaming media, prostate cancer BibRef

He, K.[Kelei], Lian, C.F.[Chun-Feng], Zhang, B.[Bing], Zhang, X.[Xin], Cao, X.H.[Xiao-Huan], Nie, D.[Dong], Gao, Y.[Yang], Zhang, J.F.[Jun-Feng], Shen, D.G.[Ding-Gang],
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images,
MedImg(40), No. 8, August 2021, pp. 2118-2128.
IEEE DOI 2108
Task analysis, Image segmentation, Computed tomography, Deformable models, Biomedical imaging, Computer architecture, consistency learning BibRef

Jia, H.Z.[Hao-Zhe], Cai, W.D.[Wei-Dong], Huang, H.[Heng], Xia, Y.[Yong],
Learning multi-scale synergic discriminative features for prostate image segmentation,
PR(126), 2022, pp. 108556.
Elsevier DOI 2204
Prostate segmentation, Intra-class consistency, Inter-class discrimination, Synergic multi-task loss BibRef

Xu, X.A.[Xuan-Ang], Sanford, T.[Thomas], Turkbey, B.[Baris], Xu, S.[Sheng], Wood, B.J.[Bradford J.], Yan, P.K.[Ping-Kun],
Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation,
MedImg(41), No. 6, June 2022, pp. 1331-1345.
IEEE DOI 2206
Image segmentation, Ultrasonic imaging, Training, Feature extraction, Biomedical imaging, Imaging, shadow artifact BibRef

Peng, T.[Tao], Zhao, J.[Jing], Gu, Y.D.[Yi-Dong], Wang, C.S.[Cai-Shan], Wu, Y.Y.[Yi-Yun], Cheng, X.X.[Xiu-Xiu], Cai, J.[Jing],
H-ProMed: Ultrasound image segmentation based on the evolutionary neural network and an improved principal curve,
PR(131), 2022, pp. 108890.
Elsevier DOI 2208
Accurate prostate segmentation, Transrectal ultrasound, Principal curve, Optimized closed polygonal segment method, Interpretable mathematical model BibRef

Peng, T.[Tao], Tang, C.Y.[Cai-Yin], Wu, Y.Y.[Yi-Yun], Cai, J.[Jing],
H-SegMed: A Hybrid Method for Prostate Segmentation in TRUS Images via Improved Closed Principal Curve and Improved Enhanced Machine Learning,
IJCV(130), No. 8, August 2022, pp. 1896-1919.
Springer DOI 2207
BibRef

Moradi, H.[Hamid], Foruzan, A.H.[Amir Hossein],
Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in MR Images,
IJIG(22), No. 4, July 2022, pp. 2250031.
DOI Link 2208
BibRef

Yang, Q.[Qianye], Atkinson, D.[David], Fu, Y.[Yunguan], Syer, T.[Tom], Yan, W.[Wen], Punwani, S.[Shonit], Clarkson, M.J.[Matthew J.], Barratt, D.C.[Dean C.], Vercauteren, T.[Tom], Hu, Y.P.[Yi-Peng],
Cross-Modality Image Registration Using a Training-Time Privileged Third Modality,
MedImg(41), No. 11, November 2022, pp. 3421-3431.
IEEE DOI 2211
Training, Imaging, Task analysis, Medical diagnostic imaging, Image registration, Prostate cancer, Magnetic resonance imaging, multi-parametric MRI BibRef

Juneja, M.[Mamta], Saini, S.K.[Sumindar Kaur], Acharjee, R.[Rajarshi], Kaul, S.[Sambhav], Thakur, N.[Niharika], Jindal, P.[Prashant],
PC-SNet for automated detection of prostate cancer in multiparametric-magnetic resonance imaging,
IJIST(32), No. 6, 2022, pp. 1861-1879.
DOI Link 2212
convolution neural network, magnetic resonance imaging, prostate cancer, segmentation BibRef

Chen, Z.[Zhen], Yang, C.[Chen], Zhu, M.[Meilu], Peng, Z.[Zhe], Yuan, Y.X.[Yi-Xuan],
Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data,
MedImg(41), No. 12, December 2022, pp. 3663-3674.
IEEE DOI 2212
Servers, Training, Medical diagnostic imaging, Computational modeling, Prototypes, Collaborative work, prostate segmentation BibRef

Hung, A.L.Y.[Alex Ling Yu], Zheng, H.X.[Hao-Xin], Miao, Q.[Qi], Raman, S.S.[Steven S.], Terzopoulos, D.[Demetri], Sung, K.[Kyunghyun],
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI,
MedImg(42), No. 1, January 2023, pp. 291-303.
IEEE DOI 2301
Image segmentation, Transformers, Magnetic resonance imaging, Standards, Image resolution, Decoding, Attention mechanism, transformer network BibRef

Baum, Z.M.C.[Zachary M. C.], Hu, Y.P.[Yi-Peng], Barratt, D.C.[Dean C.],
Meta-Learning Initializations for Interactive Medical Image Registration,
MedImg(42), No. 3, March 2023, pp. 823-833.
IEEE DOI 2303
Image registration, Biomedical imaging, Training, Annotations, Ultrasonic imaging, Task analysis, prostate cancer BibRef

Wang, F.[Fan], Xu, X.[Xuanang], Yang, D.[Defu], Chen, R.C.[Ronald C.], Royce, T.J.[Trevor J.], Wang, A.[Andrew], Lian, J.[Jun], Lian, C.F.[Chun-Feng],
Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy,
MedImg(42), No. 4, April 2023, pp. 1046-1055.
IEEE DOI 2304
Image segmentation, Task analysis, Computed tomography, Multitasking, Radiation therapy, Planning, Learning systems, computed tomography BibRef

Aleef, T.A.[Tajwar Abrar], Lobo, J.[Julio], Baghani, A.[Ali], Mohammed, S.[Shahed], Eskandari, H.[Hani], Moradi, H.[Hamid], Rohling, R.[Robert], Goldenberg, S.L.[S. Larry], Morris, W.J.[W. James], Mahdavi, S.S.[S. Sara], Salcudean, S.E.[Septimiu E.],
Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for the Prostate,
MedImg(42), No. 11, November 2023, pp. 3436-3450.
IEEE DOI 2311
BibRef

Karthik, R., Menaka, R., Siddharth, M.V., Hussain, S.[Sameeha], Siddharth, P., Won, D.[Daehan],
HMARNET: A Hierarchical Multi-Attention Residual Network for Gleason scoring of prostate cancer,
IJIST(34), No. 1, 2024, pp. e22976.
DOI Link 2401
attention, deep learning, histopathology, prostate cancer, TCGA-PRAD BibRef

Pan, X.W.[Xian-Wei], Wang, S.[Simiao], Liu, Y.[Yunan], Wen, L.J.[Li-Jie], Lu, M.Y.[Ming-Yu],
iPCa-Former: A Multi-Task Transformer Framework for Perceiving Incidental Prostate Cancer,
SPLetters(31), 2024, pp. 785-789.
IEEE DOI 2404
Feature extraction, Transformers, Task analysis, Optimization, Benchmark testing, Prostate cancer, Decoding, boundary-based mutual information loss BibRef

Zhang, Z.[Zheng], Song, Y.S.[Yu-Shan], Tan, Y.P.[Yun-P#1ng], Yan, S.[Shuo], Zhang, B.[Bo], Zhuang, Y.F.[Yu-Feng],
Segmentation assisted Prostate Cancer Grading with Multitask Collaborative Learning,
PRL(183), 2024, pp. 42-48.
Elsevier DOI 2406
Multitask collaborative learning, Prostate cancer grading, Multimodal diagnostic data BibRef

Rajagopal, A.[Abhejit], Westphalen, A.C.[Antonio C.], Velarde, N.[Nathan], Simko, J.P.[Jeffry P.], Nguyen, H.[Hao], Hope, T.A.[Thomas A.], Larson, P.E.Z.[Peder E. Z.], Magudia, K.[Kirti],
Mixed Supervision of Histopathology Improves Prostate Cancer Classification From MRI,
MedImg(43), No. 7, July 2024, pp. 2610-2622.
IEEE DOI 2407
Magnetic resonance imaging, Biopsy, Prostate cancer, Lesions, Glands, Systematics, Biological system modeling, MRI, prostate cancer, weak spatial supervision BibRef

Smith, N.J.[Nathaniel J.], Newton, D.T.[David T.], Gunderman, D.[David], Hutchins, G.D.[Gary D.],
A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies,
MedImg(43), No. 7, July 2024, pp. 2411-2419.
IEEE DOI 2407
Kinetic theory, Interpolation, Positron emission tomography, Plasmas, Estimation, Blood, Data models, Arterial input function, positron emission tomography BibRef

Wang, W.R.[Wei-Rong], Pan, B.[Bo], Ai, Y.[Yue], Li, G.[Gonghui], Fu, Y.[Yili], Liu, Y.J.[Yan-Jie],
LightCM-PNet: A lightweight pyramid network for real-time prostate segmentation in transrectal ultrasound,
PR(156), 2024, pp. 110776.
Elsevier DOI 2408
Robotic prostate biopsy, Real-time segmentation, Ultrasound image, Tokenized MLP, Pyramid network BibRef

Li, S.C.[Shi-Chang], Wu, H.J.[Hong-Jie], Tang, C.W.[Chen-Wei], Chen, D.D.[Dong-Dong], Chen, Y.[Yueyue], Mei, L.[Ling], Yang, F.[Fan], Lv, J.C.[Jian-Cheng],
Self-supervised Domain Adaptation with Significance-Oriented Masking for Pelvic Organ Prolapse detection,
PRL(185), 2024, pp. 94-100.
Elsevier DOI 2410
Pelvic organ prolapse, Domain adaptation, Vision transformer, Masked image modeling, Significance-oriented masking, Data imbalance BibRef

Matuk, J.[James], Kurtek, S.[Sebastian], Bharath, K.[Karthik],
Topo-Geometric Analysis of Variability in Point Clouds Using Persistence Landscapes,
PAMI(46), No. 12, December 2024, pp. 11035-11046.
IEEE DOI 2411
Point cloud compression, Noise, Shape, Data analysis, Arteries, Signal resolution, Prostate cancer, Amplitude-phase separation, topological data analysis BibRef

Redekop, E.[Ekaterina], Pleasure, M.[Mara], Wang, Z.C.[Zi-Chen], Sarma, K.V.[Karthik V], Kinnaird, A.[Adam], Speier, W.[William], Arnold, C.W.[Corey W],
Codebook VQ-VAE Approach for Prostate Cancer Diagnosis using Multiparametric MRI,
EnhanceMedIm24(2365-2372)
IEEE DOI 2410
Deep learning, Adaptation models, Frequency modulation, Computational modeling, Magnetic resonance imaging, Radiology, multimodal transformer BibRef

Gao, G.[Gan], Song, A.H.[Andrew H.], Wang, F.[Fiona], Brenes, D.[David], Wang, R.[Rui], Chow, S.S.L.[Sarah S.L.], Bishop, K.W.[Kevin W.], True, L.D.[Lawrence D.], Mahmood, F.[Faisal], Liu, J.T.C.[Jonathan T.C.],
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments,
CVMI24(6955-6965)
IEEE DOI 2410
Pathology, Accuracy, Reviews, Microscopy, Biopsy, Morphology, 3D pathology, Multiple-instance learning, 2.5D, Prostate cancer, Context-awareness BibRef

Ranem, A.[Amin], González, C.[Camila], Pinto-dos Santos, D.[Daniel], Bucher, A.M.[Andreas M.], Othman, A.E.[Ahmed E.], Mukhopadhyay, A.[Anirban],
Continual atlas-based segmentation of prostate MRI,
WACV24(7548-7557)
IEEE DOI Code:
WWW Link. 2404
Training, Image segmentation, Privacy, Codes, Magnetic resonance imaging, Prototypes, Rigidity, Applications BibRef

Carloni, G.[Gianluca], Pachetti, E.[Eva], Colantonio, S.[Sara],
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI,
CVAMD23(2608-2616)
IEEE DOI 2401
BibRef

Balaha, H.M.[Hossam Magdy], Ayyad, S.M.[Sarah M.], Alksas, A.[Ahmed], Elsorougy, A.[Ali], Badawy, M.A.[Mohamed Ali], Shehata, M.[Mohamed], El-Ghar, M.A.[Mohamed Abou], Ghazal, M.[Mohammed], Mahmoud, A.[Ali], Contractor, S.[Sohail], El-Baz, A.[Ayman],
Early Diagnosis of Prostate Cancer Using Parametric Estimation of IVIM from DW-MRI,
ICIP23(2910-2914)
IEEE DOI 2312
BibRef

Hammouda, K., Khalifa, F., Ghazal, M., Darwish, H.E., Yousaf, J., El-Baz, A.,
A Pyramidal CNN-Based Gleason Grading System Using Digitized Prostate Biopsy Specimens,
ICPR22(4277-4284)
IEEE DOI 2212
Deep learning, Measurement, Convolution, Biopsy, Neural networks, Convolutional neural networks, Prostate cancer BibRef

del Rio, M.[Mauro], Lianas, L.[Luca], Aspegren, O.[Oskar], Busonera, G.[Giovanni], Versaci, F.[Francesco], Zelic, R.[Renata], Vincent, P.H.[Per H.], Leo, S.[Simone], Pettersson, A.[Andreas], Akre, O.[Olof], Pireddu, L.[Luca],
AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies,
DeepHealth22(545-556).
Springer DOI 2208
BibRef

Pachetti, E.[Eva], Colantonio, S.[Sara], Pascali, M.A.[Maria Antonietta],
On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness,
MEDXF22(317-328).
Springer DOI 2208
BibRef

Peng, T.[Tao], Tang, C.Y.[Cai-Yin], Wang, J.[Jing],
Prostate Segmentation of Ultrasound Images Based on Interpretable-Guided Mathematical Model,
MMMod22(I:166-177).
Springer DOI 2203
BibRef

Deng, H.C.[Han-Chen], Cai, N.[Naxin], Peng, Y.H.[Ya-Hui],
Semi-Quantitative Analysis of DCE-MRI for Classification of the Prostate with and without Cancer,
ICIVC21(181-185)
IEEE DOI 2112
Support vector machines, Training, Sensitivity, Receivers, Linear discriminant analysis, Data mining, Prostate cancer, classification BibRef

Dhrangadhariya, A.[Anjani], Otálora, S.[Sebastian], Atzori, M.[Manfredo], Müller, H.[Henning],
Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing,
AIDP20(154-166).
Springer DOI 2103
BibRef

Rossi, A.[Alberto], Bianchini, M.[Monica], Scarselli, F.[Franco],
Robust Prostate Cancer Classification with Siamese Neural Networks,
ISVC20(II:180-189).
Springer DOI 2103
BibRef

Lyons, A.[Alexander], Rossi, A.[Alberto],
Prostate MRI Registration Using Siamese Metric Learning,
ISVC20(II:593-603).
Springer DOI 2103
BibRef

Kalapahar, A., Silva-Rodríguez, J., Colomer, A., López-Mir, F., Naranjo, V.,
Gleason Grading of Histology Prostate Images Through Semantic Segmentation via Residual U-Net,
ICIP20(2501-2505)
IEEE DOI 2011
Image segmentation, Semantics, Biopsy, Task analysis, Prostate cancer, Feature extraction, Prostate Cancer, Histology, Residual U-Net BibRef

Cao, R., Zhong, X., Scalzo, F., Raman, S., Sung, K.,
Prostate Cancer Inference via Weakly-Supervised Learning using a Large Collection of Negative MRI,
VRMI19(434-439)
IEEE DOI 2004
Magnetic resonance imaging, Principal component analysis, Lesions, Cancer, Training, Testing, Cancer Inference, Prostate MRI BibRef

Reda, I., Ghazal, M., Shalaby, A., Elmogy, M., Aboulfotouh, A., El-Ghar, M.A., El-Melegy, M., Khalil, A., Keynton, R., El-Baz, A.,
Detecting and Localizing Prostate Cancer from Diffusion-Weighted Magnetic Resonance Imaging,
ICIP19(1405-1409)
IEEE DOI 1910
Prostate cancer, computer-aided diagnosis, convolutional neural network BibRef

Firjani, A., Khalifa, F., Elnakib, A., Gimel'farb, G.L., El-Ghar, M.A.[M. Abou], Elmaghraby, A., El-Baz, A.,
A novel image-based approach for early detection of prostate cancer,
ICIP12(2849-2852).
IEEE DOI 1302
BibRef
Earlier:
3D automatic approach for precise segmentation of the prostate from Diffusion-Weighted Magnetic Resonance Imaging,
ICIP11(2285-2288).
IEEE DOI 1201
BibRef

Germanese, D.[Danila], Colantonio, S.[Sara], Caudai, C.[Claudia], Pascali, M.A.[Maria Antonietta], Barucci, A.[Andrea], Zoppetti, N.[Nicola], Agostini, S.[Simone], Bertelli, E.[Elena], Mercatelli, L.[Laura], Miele, V.[Vittorio], Carpi, R.[Roberto],
May Radiomic Data Predict Prostate Cancer Aggressiveness?,
CAIPWS19(65-75).
Springer DOI 1909
BibRef

Alkadi, R.[Ruba], El-Baz, A.[Ayman], Taher, F.[Fatma], Werghi, N.[Naoufel],
A 2.5D Deep Learning-Based Approach for Prostate Cancer Detection on T2-Weighted Magnetic Resonance Imaging,
WiCV-E18(IV:734-739).
Springer DOI 1905
BibRef

Wang, Y., Zheng, B., Gao, D., Wang, J.,
Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation,
ICPR18(3814-3819)
IEEE DOI 1812
Tumors, Image segmentation, Prostate cancer, Training, Magnetic resonance imaging, Feature extraction, Deep learning, Prostate cancer detection BibRef

Jit, D., Qian, J., Yu, J., Kurihara, T., Zhan, S.,
Automatic Prostate Segmentation on MR Images Using Enhanced Holistically-Nested Networks,
ICPR18(3820-3825)
IEEE DOI 1812
Image segmentation, Task analysis, Feature extraction, Biomedical imaging, Image edge detection, Training, Computer architecture BibRef

Reda, I., Ghazal, M., Shalaby, A., Elmogy, M., AbouEl-Fetouh, A., Ayinde, B.O., AbouEl-Ghar, M., Elmaghraby, A., Keynton, R., El-Baz, A.,
A Novel ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer,
ICPR18(3923-3928)
IEEE DOI 1812
Prostate cancer, Magnetic resonance imaging, Feature extraction, Solid modeling, Blood, Image color analysis, Prostate Cancer, Convolutional Neural Networks BibRef

Esteban, Á.E., Colomer, A., Naranjo, V., Sales, M.Á.,
Granulometry-Based Descriptor for Pathological Tissue Discrimination in Histopathological Images,
ICIP18(1413-1417)
IEEE DOI 1809
Pathology, Glands, Feature extraction, Prostate cancer, Image segmentation, Image reconstruction, Granulometry, histology BibRef

Elmahdy, M.S.[Mohamed S.], Jagt, T.[Thyrza], Yousefi, S.[Sahar], Sokooti, H.[Hessam], Zinkstok, R.[Roel], Hoogeman, M.[Mischa], Staring, M.[Marius],
Evaluation of Multi-metric Registration for Online Adaptive Proton Therapy of Prostate Cancer,
WBIR18(94-104).
Springer DOI 1806
BibRef

Sguario Coelho de Andrade, M.L., Skeika, E., Kaminski Aires, S.B.,
Segmentation of the Prostate Gland in Images Using Prior Knowledge and Level Set Method,
WVC17(31-36)
IEEE DOI 1804
biological organs, biomedical MRI, cancer, diseases, image segmentation, medical image processing, active shape model, prior-knowledge BibRef

Ghasab, M.A.J., Paplinski, A.P., Betts, J.M., Reynolds, H.M., Haworth, A.,
Automatic 3D modelling for prostate cancer brachytherapy,
ICIP17(4452-4456)
IEEE DOI 1803
Active appearance model, Image segmentation, Mathematical model, Shape, Solid modeling, Training, Visualisation BibRef

Mun, J., Jang, W.D., Sung, D.J., Kim, C.S.,
Comparison of objective functions in CNN-based prostate magnetic resonance image segmentation,
ICIP17(3859-3863)
IEEE DOI 1803
Entropy, Hamming distance, Image segmentation, Indexes, Linear programming, Training, prostate segmentation BibRef

Cho, C., Lee, Y.H., Lee, S.,
Prostate detection and segmentation based on convolutional neural network and topological derivative,
ICIP17(3071-3074)
IEEE DOI 1803
Convolutional neural networks, Databases, Image segmentation, Magnetic resonance imaging, Medical diagnostic imaging, Training, Topological Derivative BibRef

Albayrak, N.B.[Nur Banu], Yildirim, E.[Emrah], Akgul, Y.S.[Yusuf Sinan],
Prostate Size Inference from Abdominal Ultrasound Images with Patch Based Prior Information,
ACIVS17(249-259).
Springer DOI 1712
BibRef

Karimi, A.H.[Amir-Hossein], Chung, A.G.[Audrey G.], Shafiee, M.J.[Mohammad Javad], Khalvati, F.[Farzad], Haider, M.A.[Masoom A.], Ghodsi, A.[Ali], Wong, A.[Alexander],
Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification,
ICIAR17(45-53).
Springer DOI 1706
BibRef

Clark, T.[Tyler], Wong, A.[Alexander], Haider, M.A.[Masoom A.], Khalvati, F.[Farzad],
Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images,
ICIAR17(97-104).
Springer DOI 1706
BibRef

Zhang, J.J.[Jun-Jie], Baig, S.[Sameer], Wong, A.[Alexander], Haider, M.A.[Masoom A.], Khalvati, F.[Farzad],
Segmentation of Prostate in Diffusion MR Images via Clustering,
ICIAR17(471-478).
Springer DOI 1706
BibRef

Jensen, C.[Carina], Korsager, A.S.[Anne Sofie], Boesen, L.[Lars], Østergaard, L.R.[Lasse Riis], Carl, J.[Jesper],
Computer Aided Detection of Prostate Cancer on Biparametric MRI Using a Quadratic Discriminant Model,
SCIA17(I: 161-171).
Springer DOI 1706
BibRef

Perez, I.M., Toivonen, J., Movahedi, P., Merisaari, H., Pesola, M., Taimen, P., Boström, P.J., Kiviniemi, A., Aronen, H.J., Pahikkala, T., Jambor, I.,
Diffusion weighted imaging of prostate cancer: Prediction of cancer using texture features from parametric maps of the monoexponential and kurtosis functions,
IPTA16(1-6)
IEEE DOI 1703
Gabor filters BibRef

Guinin, M., Ruan, S., Dubray, B., Massoptier, L., Gardin, I.,
Feature selection and patch-based segmentation in MRI for prostate radiotherapy,
ICIP16(2663-2667)
IEEE DOI 1610
Dictionaries BibRef

Sammouda, R., Aboalsamh, H., Saeed, F.,
Comparison between K mean and fuzzy C-mean methods for segmentation of near infrared fluorescent image for diagnosing prostate cancer,
ICCVIA15(1-6)
IEEE DOI 1603
biomedical optical imaging BibRef

Bojorquez, J.Z.[Jorge Zavala], Bricq, S.[Stephanie], Walker, P.M.[Paul M.], Lalande, A.[Alain],
Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map,
ICIP15(1185-1189)
IEEE DOI 1512
MRI BibRef

Albayrak, N.B.[Nur Banu], Oktay, A.B.[Ayse Betul], Akgul, Y.S.[Yusuf Sinan],
Prostate detection from abdominal ultrasound images: A part based approach,
ICIP15(1955-1959)
IEEE DOI 1512
Abdominal Ultrasound; Graphical Model; HOG; Prostate Detection; SVM BibRef

Mahapatra, D.[Dwarikanath], Buhmann, J.M.[Joachim M.],
Visual Saliency Based Active Learning for Prostate MRI Segmentation,
MLMI15(9-16).
Springer DOI 1511
BibRef

Weingant, M.[Michaela], Reynolds, H.M.[Hayley M.], Haworth, A.[Annette], Mitchell, C.[Catherine], Williams, S.[Scott], DiFranco, M.D.[Matthew D.],
Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation,
MLMI15(280-287).
Springer DOI 1511
BibRef

Chung, A.G.[Audrey G.], Scharfenberger, C.[Christian], Khalvati, F.[Farzad], Wong, A.[Alexander], Haider, M.A.[Masoom A.],
Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection,
ICIAR15(368-376).
Springer DOI 1507
BibRef

Samarasinghe, G., Sowmya, A., Moses, D.A.,
A Semi-Quantitative Analysis Model with Parabolic Modelling for DCE-MRI Sequences of Prostate,
DICTA14(1-6)
IEEE DOI 1502
biomedical MRI BibRef

Lehaire, J.[Jerome], Flamary, R.[Remi], Rouviere, O.[Olivier], Lartizien, C.[Carole],
Computer-aided diagnostic system for prostate cancer detection and characterization combining learned dictionaries and supervised classification,
ICIP14(2251-2255)
IEEE DOI 1502
Bismuth BibRef

Niaf, E.[Emilie], Flamary, R.[Remi], Rakotomamonjy, A.[Alain], Rouviere, O.[Olivier], Lartizien, C.[Carole],
SVM with feature selection and smooth prediction in images: Application to CAD of prostate cancer,
ICIP14(2246-2250)
IEEE DOI 1502
Biomedical imaging BibRef

Gao, Q.Q.[Qin-Quan], Asthana, A.[Akshay], Tong, T.[Tong], Hu, Y.P.[Yi-Peng], Rueckert, D.[Daniel], Edwards, P.[Philip],
Hybrid Decision Forests for Prostate Segmentation in Multi-channel MR Images,
ICPR14(3298-3303)
IEEE DOI 1412
Feature extraction BibRef

McCarthy, N.[Nicholas], Cunningham, P.[Padraig], OHurley, G.[Gillian],
The Contribution of Morphological Features in the Classification of Prostate Carcinoma in Digital Pathology Images,
ICPR14(3269-3273)
IEEE DOI 1412
Accuracy BibRef

Jacobs, J.G.[Joseph G.], Panagiotaki, E.[Eleftheria], Alexander, D.C.[Daniel C.],
Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields,
MLMI14(85-92).
Springer DOI 1410
BibRef

Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
Context-Aware Anatomical Landmark Detection: Application to Deformable Model Initialization in Prostate CT Images,
MLMI14(165-173).
Springer DOI 1410
BibRef

Park, S.H.[Sang Hyun], Gao, Y.Z.[Yao-Zong], Shi, Y.H.[Ying-Huan], Shen, D.G.[Ding-Gang],
Interactive Prostate Segmentation Based on Adaptive Feature Selection and Manifold Regularization,
MLMI14(264-271).
Springer DOI 1410
BibRef

Qian, C.[Chunjun], Wang, L.[Li], Yousuf, A.[Ambereen], Oto, A.[Aytekin], Shen, D.G.[Ding-Gang],
In Vivo MRI Based Prostate Cancer Identification with Random Forests and Auto-context Model,
MLMI14(314-322).
Springer DOI 1410
BibRef

Shao, Y.Q.[Ye-Qin], Gao, Y.Z.[Yao-Zong], Yang, X.[Xin], Shen, D.G.[Ding-Gang],
CT Prostate Deformable Segmentation by Boundary Regression,
MCV14(127-136).
Springer DOI 1501
BibRef

Gao, Y.Z.[Yao-Zong], Wang, L.[Li], Shao, Y.Q.[Ye-Qin], Shen, D.G.[Ding-Gang],
Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images,
MLMI14(93-100).
Springer DOI 1410
BibRef

Cazoulat, G., Simon, A., Dumenil, A., Gnep, K., de Crevoisier, R., Acosta, O., Haigron, P.,
Surface-Constrained Nonrigid Registration for Dose Monitoring in Prostate Cancer Radiotherapy,
MedImg(33), No. 7, July 2014, pp. 1464-1474.
IEEE DOI 1407
Accuracy BibRef

Wu, Y., Liu, G., Huang, M., Guo, J., Jiang, J., Yang, W., Chen, W., Feng, Q.,
Prostate Segmentation Based on Variant Scale Patch and Local Independent Projection,
MedImg(33), No. 6, June 2014, pp. 1290-1303.
IEEE DOI 1407
Computed tomography BibRef

Li, A.[Ang], Li, C.Y.[Chang-Yang], Wang, X.Y.[Xiu-Ying], Eberl, S., Feng, D.D., Fulham, M.J.,
Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker,
DICTA13(1-7)
IEEE DOI 1402
biomedical MRI BibRef

Vafaie, R., Alirezaie, J., Babyn, P.,
Fully Automated Model-Based Prostate Boundary Segmentation Using Markov Random Field in Ultrasound Images,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Mitra, J., Kato, Z., Ghose, S., Sidibe, D., Marti, R., Llado, X., Oliver, A., Vilanova, J.C., Meriaudeau, F.,
Spectral clustering to model deformations for fast multimodal prostate registration,
ICPR12(2622-2625).
WWW Link. 1302
BibRef

Mitra, J., Ghose, S., Sidibe, D., Oliver, A., Marti, R., Llado, X., Vilanova, J.C., Comet, J., Meriaudeau, F.,
Weighted likelihood function of multiple statistical parameters to retrieve 2D TRUS-MR slice correspondence for prostate biopsy,
ICIP12(2949-2952).
IEEE DOI 1302
BibRef

Moradi, M.[Mehdi], Wachinger, C.[Christian], Fedorov, A.[Andriy], Wells, W.M.[William M.], Kapur, T.[Tina], Wolfsberger, L.D.[Luciant D.], Nguyen, P.[Paul], Tempany, C.M.[Clare M.],
MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra,
MLMI12(19-26).
Springer DOI 1211
BibRef

Shi, Y.H.[Ying-Huan], Liao, S.[Shu], Gao, Y.Z.[Yao-Zong], Zhang, D.Q.[Dao-Qiang], Gao, Y.[Yang], Shen, D.G.[Ding-Gang],
Prostate Segmentation in CT Images via Spatial-Constrained 'http://dx.doi.org/10.1007/978-3-642-35428-1_1',
1211
BibRef

Skalski, A.[Andrzej], Kos, A.[Artur], Zielinski, T.[Tomasz],
Using ASM in CT Data Segmentaion for Prostate Radiotherapy,
ICCVG12(610-617).
Springer DOI 1210
BibRef

Ghose, S.[Soumya], Mitra, J.[Jhimli], Oliver, A.[Arnau], Martí, R.[Robert], Lladó, X.[Xavier], Freixenet, J.[Jordi], Vilanova, J.C.[Joan C.], Comet, J.[Josep], Sidibé, D.[Désiré], Meriaudeau, F.[Fabrice],
A Supervised Learning Framework for Automatic Prostate Segmentation in Trans Rectal Ultrasound Images,
ACIVS12(190-200).
Springer DOI 1209
BibRef

Ghose, S., Mitra, J., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J.C., Sidibe, D., Meriaudeau, F.,
A coupled schema of probabilistic atlas and statistical shape and appearance model for 3D prostate segmentation in MR images,
ICIP12(541-544).
IEEE DOI 1302
BibRef

Ghose, S., Mitra, J., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J.C., Comet, J., Sidibe, D., Meriaudeau, F.,
A Mumford-Shah functional based variational model with contour, shape, and probability prior information for prostate segmentation,
ICPR12(121-124).
WWW Link. 1302
BibRef

Ghose, S., Mitra, J., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J.C., Sidibe, D., Meriaudeau, F.,
Graph cut energy minimization in a probabilistic learning framework for 3D prostate segmentation in MRI,
ICPR12(125-128).
WWW Link. 1302
BibRef

Ghose, S., Oliver, A., Marti, R., Llado, X., Freixenet, J., Mitra, J., Vilanova, J.C., Comet, J., Meriaudeau, F.,
Statistical Shape and Probability Prior Model for Automatic Prostate Segmentation,
DICTA11(340-345).
IEEE DOI 1205
BibRef

Mitra, J., Kato, Z., Marti, R., Oliver, A., Llado, X., Ghose, S., Vilanova, J.C., Meriaudeau, F.,
A Non-Linear Diffeomorphic Framework for Prostate Multimodal Registration,
DICTA11(31-36).
IEEE DOI 1205
BibRef

Mitra, J.[Jhimli], Oliver, A.[Arnau], Marti, R.[Robert], Llado, X.[Xavier], Vilanova, J.C.[Joan C.], Meriaudeau, F.[Fabrice],
Multimodal Prostate Registration Using Thin-Plate Splines from Automatic Correspondences,
DICTA10(587-592).
IEEE DOI 1012
BibRef

Ghose, S., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J.C., Meriaudeau, F.,
A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance,
ICIP11(713-716).
IEEE DOI 1201
BibRef

Lu, C.[Chao], Chelikani, S.[Sudhakar], Papademetris, X.[Xenophon], Staib, L.[Lawrence], Duncan, J.S.[James S.],
Constrained non-rigid registration using Lagrange multipliers for application in prostate radiotherapy,
MMBIA10(133-138).
IEEE DOI 1006
BibRef

Xia, T.[Tian], Yu, Y.Z.[Yi-Zhou], Hua, J.[Jing],
Automatic detection of malignant prostatic gland units in cross-sectional microscopic images,
ICIP10(1057-1060).
IEEE DOI 1009
BibRef

Zaboli, S.[Shiva], Tabibiazar, A.[Arash], Fieguth, P.W.[Paul W.],
Organ Recognition Using Gabor Filters,
CRV10(94-100).
IEEE DOI 1005
E.g. prostate tissue using techniques similar to iris recognition. BibRef

Ou, Y.M.[Yang-Ming], Shen, D.G.[Ding-Gang], Feldman, M.D.[Michael D.], Tomaszewski, J.E.[John E.], Davatzikos, C.[Christos],
Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework,
MMBIA09(125-132).
IEEE DOI 0906
BibRef

Arif, M.[Muhammad], Rajpoot, N.[Nasir],
Classification of potential nuclei in prostate histology images using shape manifold learning,
ICMV07(113-118).
IEEE DOI 0712
BibRef

Jendoubi, A., Zeng, J.C.[Jian-Chao], Chouikha, M.F.,
Top-down approach to segmentation of prostate boundaries in ultrasound images,
AIPR04(145-149).
IEEE DOI 0410
BibRef

Zaim, A.[Amjad],
FSM: A new finite sphere method for modeling 3D geometry of the prostate,
ICIP08(2956-2959).
IEEE DOI 0810
BibRef

Zouqi, M.[Mehrnaz], Samarabandu, J.[Jagath],
Prostate Segmentation from 2-D Ultrasound Images Using Graph Cuts and Domain Knowledge,
CRV08(359-362).
IEEE DOI 0805
BibRef

Hui, E.K.T.[Eric K.T.], Mohamed, S.S., Salama, M.M.A., Fenster, A.,
Prostate TRUS Image Region-Based Feature Extraction and Evaluation,
ICIAR09(759-771).
Springer DOI 0907
BibRef

Hui, E.K.T.[Eric K.T.], Mohamed, S.S., Salama, M.M.A., Rizkalla, K.,
Region-Based Feature Extraction Using TRUS Images,
Southwest08(205-208).
IEEE DOI 0803
BibRef

Li, J., Mohamed, S.S., Salama, M.M.A., Freeman, G.H.,
Prostate Tissue Texture Feature Extraction for Cancer Recognition in TRUS Images Using Wavelet Decomposition,
ICIAR07(993-1004).
Springer DOI 0708
BibRef

Mohamed, S.S., Salama, M.M.A., Kamel, M., Rizkalla, K.,
Region of Interest Based Prostate Tissue Characterization Using Least Square Support Vector Machine LS-SVM,
ICIAR04(II: 51-58).
Springer DOI 0409
BibRef

Bougioukos, P.[Panagiotis], Cavouras, D.[Dionisis], Daskalakis, A.[Antonis], Kalatzis, I.[Ioannis], Kostopoulos, S.[Spiros], Georgiadis, P.[Pantelis], Nikiforidis, G.[George], Bezerianos, A.[Anastasios],
Biomarker Selection System, Employing an Iterative Peak Selection Method, for Identifying Biomarkers Related to Prostate Cancer,
CAIP07(197-204).
Springer DOI 0708
BibRef

Rodríguez-Vila, B.[Borja], Pettersson, J.[Johanna], Borga, M.[Magnus], García-Vicente, F.[Feliciano], Gómez, E.J.[Enrique J.], Knutsson, H.[Hans],
3D Deformable Registration for Monitoring Radiotherapy Treatment in Prostate Cancer,
SCIA07(750-759).
Springer DOI 0706
BibRef

Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.,
Automated Snake Initialization for the Segmentation of the Prostate in Ultrasound Images,
ICIAR05(930-937).
Springer DOI 0509
BibRef

Zaim, A.[Amjad],
Automatic Segmentation of the Prostate from Ultrasound Data Using Feature-Based Self Organizing Map,
SCIA05(1259-1265).
Springer DOI 0506
BibRef

Cosío, F.A.[Fernando Arámbula],
Robust Fitting of a Point Distribution Model of the Prostate Using Genetic Algorithms,
ICIAR04(II: 76-83).
Springer DOI 0409
BibRef

Rousson, M.[Mikael], Khamene, A.[Ali], Diallo, M.[Mamadou], Celi, J.C.[Juan Carlos], Sauer, F.[Frank],
Constrained Surface Evolutions for Prostate and Bladder Segmentation in CT Images,
CVBIA05(251-260).
Springer DOI 0601
BibRef

Sahba, F., Tizhoosh, H.R., Salama, M.M.A.,
Segmentation of Prostate Boundaries Using Regional Contrast Enhancement,
ICIP05(II: 1266-1269).
IEEE DOI 0512
BibRef

Yu, Y., Acton, S.T., Thornton, K.,
Detection of Radioactive Seeds in Ultrasound Images of the Prostate,
ICIP01(II: 319-322).
IEEE DOI 0108
BibRef

Pouliot, S., Zaccarin, A., Laurendeau, D., Pouliot, J.,
Automatic Detection of Three Radio-opaque Markers for Prostate Targeting Using Epid During External Radiation Therapy,
ICIP01(II: 857-860).
IEEE DOI 0108
BibRef

Adiga, P.S.U., Chaudhuri, B.B.,
Automatic Prostate Cancer Grading System Based on 3-D Histo-pathological Images,
MVA98(xx-yy). BibRef 9800

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Medical Applications -- Cervical Cancer Analysis, Ovarian Cancer .

Last update:Nov 26, 2024 at 16:40:19