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Biomedical image processing
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1502
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1603
Biomedical imaging
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Histopathology images, Classification, Retrieval,
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Image segmentation, Imaging, Diseases, Spatial resolution, Chemicals,
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Image color analysis, Histograms, Image analysis,
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Discriminative feature learning,
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Cancer, Image analysis, Training, Task analysis,
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Image segmentation, Pathology, Training, Diseases, Task analysis,
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2011
Image color analysis, Parameter estimation, Pathology,
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Image segmentation, Annotations, Training, Task analysis, Cancer,
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2012
Image segmentation, Standards, Task analysis, Pathology,
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AttentionBoost: Learning What to Attend for Gland Segmentation in
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2012
Glands, Task analysis, Image segmentation, Adaptation models,
Training, Boosting, Deep learning,
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2103
Histopathology, Cancer, Feature extraction, Databases,
Solid modeling, Image analysis, Annotations, Digital pathology, RNN
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2110
Task analysis, Annotations, Histopathology,
Semisupervised learning, Training, Tumors, Labeling, domain adaptation
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IEEE DOI
2110
Uncertainty, Feature extraction, Histograms, Training,
Image segmentation, Histopathology, Entropy, Interpretability,
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2110
Pathology, Image segmentation, Decoding, Decision trees,
Convolutional codes, Feature extraction, Computer architecture,
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Shao, W.[Wei],
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Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From
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2112
Predictive models, Cancer, Computational modeling,
Prognostics and health management, Tumors, Hazards,
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Yu, Y.B.[Yong-Bin],
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Wang, X.X.[Xiang-Xiang],
DHS-CapsNet: Dual horizontal squash capsule networks for lung and
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DOI Link
2112
artificial intelligence, capsule network, colon cancer,
convolutional neural network, histopathological images, lung cancer
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Hu, K.[Kai],
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A hierarchical and multi-view registration of serial
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2112
Image registration, Histopathological image, Multi-view,
Elastic registration, Biomarker colocalization
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Intra- and Inter-Pair Consistency for Semi-Supervised Gland
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IP(31), 2022, pp. 894-905.
IEEE DOI
2201
Glands, Image segmentation, Semantics, Feature extraction,
Histopathology, Training, Data models, Gland segmentation,
deep convolutional neural network
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Zhang, J.[Jun],
Lei, F.Q.[Fu-Qiang],
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Big-Hypergraph Factorization Neural Network for Survival Prediction
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IP(31), 2022, pp. 1149-1160.
IEEE DOI
2202
Feature extraction, Predictive models, Correlation, Hazards,
Data models, Convolutional neural networks, Visualization, survival prediction
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Rony, J.[Jérôme],
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Deep Interpretable Classification and Weakly-Supervised Segmentation
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IEEE DOI
2203
Image segmentation, Uncertainty, Histopathology, Predictive models,
Standards, Training, Solid modeling, interpretability
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Zhu, C.[Chuang],
Chen, W.K.[Wen-Kai],
Peng, T.[Ting],
Wang, Y.[Ying],
Jin, M.[Mulan],
Hard Sample Aware Noise Robust Learning for Histopathology Image
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MedImg(41), No. 4, April 2022, pp. 881-894.
IEEE DOI
2204
Noise measurement, Training, Histopathology, Noise robustness,
Image classification, Data models, Predictive models,
label correction
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Li, W.Y.[Wen-Yuan],
Li, J.[Jiayun],
Wang, Z.C.[Zi-Chen],
Polson, J.[Jennifer],
Sisk, A.E.[Anthony E.],
Sajed, D.P.[Dipti P.],
Speier, W.[William],
Arnold, C.W.[Corey W.],
PathAL: An Active Learning Framework for Histopathology Image
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MedImg(41), No. 5, May 2022, pp. 1176-1187.
IEEE DOI
2205
Noise measurement, Annotations, Training, Biomedical imaging,
Uncertainty, Image segmentation, Task analysis,
curriculum learning
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Chattopadhyay, A.[Aratrik],
Paul, A.[Angshuman],
Mukherjee, D.P.[Dipti Prasad],
Detail preserving conditional random field as 2-D RNN for gland
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PRL(159), 2022, pp. 38-45.
Elsevier DOI
2206
2-D RNN, Conditional random field, Detail preservation,
Gland segmentation, Histology
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Xiang, T.[Tiange],
Song, Y.[Yang],
Zhang, C.Y.[Chao-Yi],
Liu, D.[Dongnan],
Chen, M.[Mei],
Zhang, F.[Fan],
Huang, H.[Heng],
O'Donnell, L.[Lauren],
Cai, W.D.[Wei-Dong],
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel
Pathology Image Analysis,
MedImg(41), No. 8, August 2022, pp. 2180-2190.
IEEE DOI
2208
Visualization, Encoding, Annotations, Redundancy, Pathology,
Metastasis, Image analysis, Weakly-supervised training,
whole slide images
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Xu, Y.Z.[Yong-Zhao],
dos Santos, M.A.[Matheus A.],
Souza, L.F.F.[Luís Fabrício F.],
Marques, A.G.[Adriell G.],
Zhang, L.J.[Li-Juan],
da Costa Nascimento, J.J.[José Jerovane],
de Albuquerque, V.H.C.[Victor Hugo C.],
Filho, P.P.R.[Pedro P. Rebouças],
New fully automatic approach for tissue identification in
histopathological examinations using transfer learning,
IET-IPR(16), No. 11, 2022, pp. 2875-2889.
DOI Link
2208
BibRef
Lin, J.[Jiatai],
Han, G.Q.[Guo-Qiang],
Pan, X.P.[Xi-Peng],
Liu, Z.[Zaiyi],
Chen, H.[Hao],
Li, D.[Danyi],
Jia, X.P.[Xi-Ping],
Shi, Z.W.[Zhen-Wei],
Wang, Z.Z.[Zhi-Zhen],
Cui, Y.F.[Yan-Fen],
Li, H.M.[Hai-Ming],
Liang, C.H.[Chang-Hong],
Liang, L.[Li],
Wang, Y.[Ying],
Han, C.[Chu],
PDBL: Improving Histopathological Tissue Classification With
Plug-and-Play Pyramidal Deep-Broad Learning,
MedImg(41), No. 9, September 2022, pp. 2252-2262.
IEEE DOI
2209
Feature extraction, Computational modeling, Training,
Adaptation models, Biomedical imaging, Annotations, Deep learning,
broad learning system
BibRef
Ge, L.[Lin],
Wei, X.Y.[Xing-Yue],
Hao, Y.[Yayu],
Luo, J.W.[Jian-Wen],
Xu, Y.[Yan],
Unsupervised Histological Image Registration Using Structural Feature
Guided Convolutional Neural Network,
MedImg(41), No. 9, September 2022, pp. 2414-2431.
IEEE DOI
2209
Image registration, Strain, Convolutional neural networks,
Task analysis, Image resolution, Feature extraction,
unsupervised learning
BibRef
Zhang, Y.L.[Yun-Long],
Lin, X.[Xin],
Zhuang, Y.H.[Yi-Hong],
Sun, L.Y.[Li-Yan],
Huang, Y.[Yue],
Ding, X.[Xinghao],
Wang, G.S.[Gui-Sheng],
Yang, L.[Lin],
Yu, Y.Z.[Yi-Zhou],
Harmonizing Pathological and Normal Pixels for Pseudo-Healthy
Synthesis,
MedImg(41), No. 9, September 2022, pp. 2457-2468.
IEEE DOI
2209
Pathology, Image segmentation, Training, Lesions, Biomedical imaging,
Generators, Measurement, Medical image synthesis,
label noise
BibRef
Zheng, Y.[Yi],
Gindra, R.H.[Rushin H.],
Green, E.J.[Emily J.],
Burks, E.J.[Eric J.],
Betke, M.[Margrit],
Beane, J.E.[Jennifer E.],
Kolachalama, V.B.[Vijaya B.],
A Graph-Transformer for Whole Slide Image Classification,
MedImg(41), No. 11, November 2022, pp. 3003-3015.
IEEE DOI
2211
Pathology, Feature extraction, Transformers, Tumors, Deep learning,
Training, Lung, Digital pathology, graph convolutional network,
lung cancer
BibRef
Shen, Y.Q.[Yi-Qing],
Shen, D.G.[Ding-Gang],
Ke, J.[Jing],
Identify Representative Samples by Conditional Random Field of Cancer
Histology Images,
MedImg(41), No. 12, December 2022, pp. 3835-3848.
IEEE DOI
2212
Histopathology, Training, Task analysis,
Convolutional neural networks, Deep learning, Correlation, active learning
BibRef
Zhang, W.H.[Wen-Hua],
Zhang, J.[Jun],
Yang, S.[Sen],
Wang, X.[Xiyue],
Yang, W.[Wei],
Huang, J.Z.[Jun-Zhou],
Wang, W.P.[Wen-Ping],
Han, X.[Xiao],
Knowledge-Based Representation Learning for Nucleus Instance
Classification From Histopathological Images,
MedImg(41), No. 12, December 2022, pp. 3939-3951.
IEEE DOI
2212
Pathology, Task analysis, Feature extraction, Data models,
Representation learning, Labeling, Annotations, Triplet learning,
digital pathology
BibRef
Gao, Z.[Zeyu],
Jia, C.[Chang],
Li, Y.[Yang],
Zhang, X.[Xianli],
Hong, B.[Bangyang],
Wu, J.[Jialun],
Gong, T.[Tieliang],
Wang, C.B.[Chun-Bao],
Meng, D.Y.[De-Yu],
Zheng, Y.F.[Ye-Feng],
Li, C.[Chen],
Unsupervised Representation Learning for Tissue Segmentation in
Histopathological Images: From Global to Local Contrast,
MedImg(41), No. 12, December 2022, pp. 3611-3623.
IEEE DOI
2212
Task analysis, Image segmentation, Annotations, Decoding, Tumors,
Representation learning, Cancer, Contrastive learning, superpixel
BibRef
Yang, M.[Mei],
Xie, Z.[Zhiying],
Wang, Z.X.[Zhao-Xia],
Yuan, Y.[Yun],
Zhang, J.[Jue],
Su-MICL: Severity-Guided Multiple Instance Curriculum Learning for
Histopathology Image Interpretable Classification,
MedImg(41), No. 12, December 2022, pp. 3533-3543.
IEEE DOI
2212
Histopathology, Lesions, Training, Diseases, Annotations,
Task analysis, Supervised learning, Multiple instance learning,
interpretability
BibRef
Wang, Z.Z.[Zhen-Zhen],
Saoud, C.[Carla],
Wangsiricharoen, S.[Sintawat],
James, A.W.[Aaron W.],
Popel, A.S.[Aleksander S.],
Sulam, J.[Jeremias],
Label Cleaning Multiple Instance Learning: Refining Coarse
Annotations on Single Whole-Slide Images,
MedImg(41), No. 12, December 2022, pp. 3952-3968.
IEEE DOI
2212
Annotations, Cancer, Pathology, Training, Tumors, Refining,
Predictive models, Whole-slide image segmentation, label cleaning
BibRef
Chen, Y.[Yi],
Dong, Y.[Yang],
Si, L.[Lu],
Yang, W.M.[Wen-Ming],
Du, S.[Shan],
Tian, X.[Xuewu],
Li, C.[Chao],
Liao, Q.M.[Qing-Min],
Ma, H.[Hui],
Dual Polarization Modality Fusion Network for Assisting Pathological
Diagnosis,
MedImg(42), No. 1, January 2023, pp. 304-316.
IEEE DOI
2301
Cancer, Pathology, Imaging, Feature extraction, Microstructure,
Optical switches, Image classification, switched attention
BibRef
Teh, E.W.[Eu Wern],
Taylor, G.W.[Graham W.],
Understanding the impact of image and input resolution on deep
digital pathology patch classifiers,
CRV22(159-166)
IEEE DOI
2301
Pathology, Image resolution, Correlation, Annotations, Data models, Tuning,
Robots, Digital Pathology, Patch Classification, Annotation-efficient Learning
BibRef
Li, M.[Meng],
Li, C.Y.[Chao-Yi],
Hobson, P.[Peter],
Jennings, T.[Tony],
Lovell, B.C.[Brian C.],
MedViTGAN: End-to-End Conditional GAN for Histopathology Image
Augmentation with Vision Transformers,
ICPR22(4406-4413)
IEEE DOI
2212
Training, Adaptation models, Histopathology, Image synthesis,
Semantic segmentation, Computer architecture, Transformers, Vision transformer
BibRef
Alhammad, S.[Sarah],
Zhang, T.[Teng],
Zhao, K.[Kun],
Hobson, P.[Peter],
Jennings, A.[Anthony],
Lovell, B.C.[Brian C.],
Efficient Cell Labelling for Gram Stain WSIs,
ICPR22(4226-4233)
IEEE DOI
2212
Training, Pathology, Annotations, Scholarships, Manuals, Detectors,
Transformers, WSI, Gram Stain Analysis, Detection, CNN, Cell Counting,
Microbiology
BibRef
Launet, L.[Laëtitia],
Colomer, A.[Adrián],
Mosquera-Zamudio, A.[Andrés],
Moscardó, A.[Anaďs],
Monteagudo, C.[Carlos],
Naranjo, V.[Valery],
A Self-Training Weakly-Supervised Framework for Pathologist-Like
Histopathological Image Analysis,
ICIP22(3401-3405)
IEEE DOI
2211
Training, Pathology, Image analysis, Annotations,
Biological system modeling, Data models, Skin, self-training,
whole slide images
BibRef
Si, Y.X.[Yu-Xuan],
Fang, Z.Q.[Zheng-Qing],
Kuang, K.[Kun],
Huang, Z.X.[Zheng-Xing],
Yao, Y.F.[Yu-Feng],
Wu, F.[Fei],
Disentangled Sequential Autoencoder with Local Consistency for
Infectious Keratitis Diagnosis,
ICIP22(3893-3897)
IEEE DOI
2211
Deep learning, Pathology, Pathogens, Shape, Visual impairment,
Time series analysis, Inference algorithms,
Infectious Keratitis
BibRef
Lotfollahi, M.[Mahsa],
Tran, N.[Nguyen],
Gajjela, C.[Chalapathi],
Berisha, S.[Sebastian],
Han, Z.[Zhu],
Mayerich, D.[David],
Reddy, R.[Rohith],
Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging,
ICIP22(2336-2340)
IEEE DOI
2211
Measurement, Tensors, Image coding, Image synthesis, Histopathology,
Training data, Spatial resolution, Adaptive Sampling,
SVM Classification Metric
BibRef
Dwivedi, C.[Chaitanya],
Nofallah, S.[Shima],
Pouryahya, M.[Maryam],
Iyer, J.[Janani],
Leidal, K.[Kenneth],
Chung, C.H.[Chu-Han],
Watkins, T.[Timothy],
Billin, A.[Andrew],
Myers, R.[Robert],
Abel, J.[John],
Behrooz, A.[Ali],
Multi stain graph fusion for multimodal integration in pathology,
CVMI22(1834-1844)
IEEE DOI
2210
Weight measurement, Histopathology, Computational modeling,
Conferences, Liver, Predictive models
BibRef
Alali, M.H.[Mohammed H.],
Roohi, A.[Arman],
Deogun, J.S.[Jitender S.],
Enabling Efficient Training of Convolutional Neural Networks for
Histopathology Images,
DeepHealth22(533-544).
Springer DOI
2208
BibRef
Gräbel, P.[Philipp],
Thull, J.[Julian],
Crysandt, M.[Martina],
Klinkhammer, B.M.[Barbara M.],
Boor, P.[Peter],
Brümmendorf, T.H.[Tim H.],
Merhof, D.[Dorit],
Spatial Maturity Regression for the Classification of Hematopoietic
Cells,
IPTA22(1-6)
IEEE DOI
2206
Visualization, Microscopy, Image processing, Neural networks,
Cells (biology), Bones, Blood, representation learning,
em-bedding guides
BibRef
Azizi, S.[Shekoofeh],
Mustafa, B.[Basil],
Ryan, F.[Fiona],
Beaver, Z.[Zachary],
Freyberg, J.[Jan],
Deaton, J.[Jonathan],
Loh, A.[Aaron],
Karthikesalingam, A.[Alan],
Kornblith, S.[Simon],
Chen, T.[Ting],
Natarajan, V.[Vivek],
Norouzi, M.[Mohammad],
Big Self-Supervised Models Advance Medical Image Classification,
ICCV21(3458-3468)
IEEE DOI
2203
Pathology, Image recognition, Annotations, Dermatology,
Digital cameras, Task analysis, Medical, biological,
BibRef
Abousamra, S.[Shahira],
Belinsky, D.[David],
van Arnam, J.[John],
Allard, F.[Felicia],
Yee, E.[Eric],
Gupta, R.[Rajarsi],
Kurc, T.[Tahsin],
Samaras, D.[Dimitris],
Saltz, J.[Joel],
Chen, C.[Chao],
Multi-Class Cell Detection Using Spatial Context Representation,
ICCV21(3985-3994)
IEEE DOI
2203
Representation learning, Multiplexing, Pathology,
Clustering methods, Topology, Task analysis, Medical, biological,
BibRef
Wang, H.T.[Hao-Tian],
Xian, M.[Min],
Vakanski, A.[Aleksandar],
TA-Net: Topology-Aware Network for Gland Segmentation,
WACV22(3241-3249)
IEEE DOI
2202
Image segmentation, Network topology, Histopathology, Semantics,
Glands, Morphology, Computer architecture, Grouping and Shape
BibRef
Sahel, Y.B.[Yair Ben],
Dardikman-Yoffe, G.[Gilli],
Eldar, Y.C.[Yonina C.],
Gosh, S.[Shirsendu],
Haran, G.[Gilad],
Super-Resolved Imaging of Early-Stage Dynamics in the Immune Response,
ICIP21(3468-3472)
IEEE DOI
2201
Location awareness, Surface reconstruction, Diffraction,
Microscopy, Superresolution, Real-time systems, Surface topography,
High-Resolution Imaging
BibRef
Alhammad, S.[Sarah],
Zhao, K.[Kun],
Jennings, A.[Anthony],
Hobson, P.[Peter],
Smith, D.F.[Daniel F.],
Baker, B.[Brett],
Staweno, J.[Justin],
Lovell, B.C.[Brian C.],
Efficient DNN-Based Classification of Whole Slide Gram Stain Images
for Microbiology,
DICTA21(01-08)
IEEE DOI
2201
Training, Deep learning, Pathology, Microorganisms, Protocols, Oils,
Microscopy, Bacteria Classification, DNN, Computer Aided Diagnosis,
Digital Pathology
BibRef
Shen, Y.Q.[Yi-Qing],
Ke, J.[Jing],
Su-Sampling Based Active Learning for Large-Scale Histopathology
Image,
ICIP21(116-120)
IEEE DOI
2201
Deep learning, Image segmentation, Uncertainty,
Monte Carlo methods, Annotations, Histopathology, Neural networks,
convolutional neural network
BibRef
Dodballapur, V.[Veena],
Song, Y.[Yang],
Huang, H.[Heng],
Chen, M.[Mei],
Chrzanowski, W.[Wojciech],
Cai, W.D.[Wei-Dong],
Dual-Stage Domain Adaptive Mitosis Detection for Histopathology
Images,
DICTA20(1-7)
IEEE DOI
2201
Training, Adaptive systems, Histopathology, Neural networks,
Pipelines, Machine learning, Testing, Domain adaptation, mitosis,
convolutional neural networks
BibRef
Gräbel, P.[Philipp],
Crysandt, M.[Martina],
Klinkhammer, B.M.[Barbara M.],
Boor, P.[Peter],
Brümmendorf, T.H.[Tim H.],
Merhof, D.[Dorit],
Guided Representation Learning for the Classification of
Hematopoietic Cells,
CDPath21(545-551)
IEEE DOI
2112
Training, Dimensionality reduction, Image analysis, Microscopy,
Knowledge based systems, Throughput
BibRef
Pahwa, E.[Esha],
Mehta, D.[Dwij],
Kapadia, S.[Sanjeet],
Jain, D.[Devansh],
Luthra, A.[Achleshwar],
MedSkip: Medical Report Generation Using Skip Connections and
Integrated Attention,
CVAMD21(3402-3408)
IEEE DOI
2112
Visualization, Pathology, Computer architecture,
Radiology, Transformers, Feature extraction
BibRef
Dawood, M.[Muhammad],
Branson, K.[Kim],
Rajpoot, N.M.[Nasir M.],
Minhas, F.U.A.A.[Fayyaz Ul Amir Afsar],
ALBRT: Cellular Composition Prediction in Routine Histology Images,
CDPath21(664-673)
IEEE DOI
2112
Codes, Histopathology, Topology, Task analysis, Tumors
BibRef
Jahanifar, M.[Mostafa],
Tajeddin, N.Z.[Neda Zamani],
Koohbanani, N.A.[Navid Alemi],
Rajpoot, N.[Nasir],
Robust Interactive Semantic Segmentation of Pathology Images with
Minimal User Input,
CDPath21(674-683)
IEEE DOI
2112
Geometry, Deep learning, Image segmentation, Histopathology,
Annotations, Computational modeling, Semantics
BibRef
Jewsbury, R.[Robert],
Bhalerao, A.[Abhir],
Rajpoot, N.[Nasir],
A QuadTree Image Representation for Computational Pathology,
CDPath21(648-656)
IEEE DOI
2112
Visualization, Histopathology, Pipelines,
Data visualization, Image representation, Prediction algorithms
BibRef
Boyd, J.[Joseph],
Liashuha, M.[Mykola],
Deutsch, E.[Eric],
Paragios, N.[Nikos],
Christodoulidis, S.[Stergios],
Vakalopoulou, M.[Maria],
Self-Supervised Representation Learning using Visual Field Expansion
on Digital Pathology,
CDPath21(639-647)
IEEE DOI
2112
Visualization, Codes, Histopathology,
Computational modeling, Tools
BibRef
Lai, Z.F.[Zheng-Feng],
Wang, C.[Chao],
Oliveira, L.C.[Luca Cerny],
Dugger, B.N.[Brittany N.],
Cheung, S.C.[Sen-Ching],
Chuah, C.N.[Chen-Nee],
Joint Semi-supervised and Active Learning for Segmentation of
Gigapixel Pathology Images with Cost-Effective Labeling,
CDPath21(591-600)
IEEE DOI
2112
Training, Deep learning, Pathology, Image segmentation,
Image analysis, Manuals
BibRef
Marini, N.[Niccolň],
Atzori, M.[Manfredo],
Otálora, S.[Sebastian],
Marchand-Maillet, S.[Stephane],
Müller, H.[Henning],
H&E-adversarial network: a convolutional neural network to learn
stain-invariant features through Hematoxylin & Eosin regression,
CDPath21(601-610)
IEEE DOI
2112
Training, Image segmentation, Image color analysis, Histopathology,
Neural networks, Convolutional neural networks
BibRef
Weitz, P.[Philippe],
Wang, Y.[Yinxi],
Hartman, J.[Johan],
Rantalainen, M.[Mattias],
An investigation of attention mechanisms in histopathology
whole-slide-image analysis for regression objectives,
CDPath21(611-619)
IEEE DOI
2112
Analytical models, Histopathology,
Computational modeling, Focusing, Predictive models
BibRef
Deuschel, J.[Jessica],
Firmbach, D.[Daniel],
Geppert, C.I.[Carol I.],
Eckstein, M.[Markus],
Hartmann, A.[Arndt],
Bruns, V.[Volker],
Kuritcyn, P.[Petr],
Dexl, J.[Jakob],
Hartmann, D.[David],
Perrin, D.[Dominik],
Wittenberg, T.[Thomas],
Benz, M.[Michaela],
Multi-Prototype Few-shot Learning in Histopathology,
CDPath21(620-628)
IEEE DOI
2112
Training, Degradation, Histopathology, Neural networks,
Prototypes, Distributed databases
BibRef
Srinidhi, C.L.[Chetan L.],
Martel, A.L.[Anne L.],
Improving Self-supervised Learning with Hardness-aware Dynamic
Curriculum Learning: An Application to Digital Pathology,
CDPath21(562-571)
IEEE DOI
2112
Training, Visualization, Histopathology, Annotations,
Benchmark testing, Robustness, Complexity theory
BibRef
Tang, S.[Sheyang],
Hosseini, M.S.[Mahdi S.],
Chen, L.[Lina],
Varma, S.[Sonal],
Rowsell, C.[Corwyn],
Damaskinos, S.[Savvas],
Plataniotis, K.N.[Konstantinos N.],
Wang, Z.[Zhou],
Probeable DARTS with Application to Computational Pathology,
CDPath21(572-581)
IEEE DOI
2112
Measurement, Knowledge engineering, Pathology,
Computer network reliability, Robustness
BibRef
Gamper, J.[Jevgenij],
Rajpoot, N.[Nasir],
Multiple Instance Captioning: Learning Representations from
Histopathology Textbooks and Articles,
CVPR21(16544-16554)
IEEE DOI
2111
Histopathology, Computational modeling,
Estimation, Pattern recognition, Task analysis
BibRef
Zhang, J.W.[Jing-Wei],
Ma, K.[Ke],
van Arnam, J.[John],
Gupta, R.[Rajarsi],
Saltz, J.[Joel],
Vakalopoulou, M.[Maria],
Samaras, D.[Dimitris],
A Joint Spatial and Magnification Based Attention Framework for Large
Scale Histopathology Classification,
CVMI21(3771-3779)
IEEE DOI
2109
Training, Deep learning, Histopathology, Microscopy, Tools,
Probability distribution, Pattern recognition
BibRef
tepec, D.[Dejan],
Skocaj, D.[Danijel],
Unsupervised Detection of Cancerous Regions in Histology Imagery
using Image-to-Image Translation,
CVMI21(3780-3787)
IEEE DOI
2109
Visualization, Image analysis, Histopathology,
Biomedical measurement, Pattern recognition
BibRef
Wei, J.[Jerry],
Suriawinata, A.[Arief],
Ren, B.[Bing],
Liu, X.Y.[Xiao-Ying],
Lisovsky, M.[Mikhail],
Vaickus, L.[Louis],
Brown, C.[Charles],
Baker, M.[Michael],
Nasir-Moin, M.[Mustafa],
Tomita, N.[Naofumi],
Torresani, L.[Lorenzo],
Wei, J.[Jason],
Hassanpour, S.[Saeed],
Learn like a Pathologist: Curriculum Learning by Annotator Agreement
for Histopathology Image Classification,
WACV21(2472-2482)
IEEE DOI
2106
Training, Learning systems, Histopathology,
Task analysis, Image classification
BibRef
Belharbi, S.[Soufiane],
Ben Ayed, I.[Ismail],
McCaffrey, L.[Luke],
Granger, E.[Eric],
Deep Active Learning for Joint Classification Segmentation with Weak
Annotator,
WACV21(3337-3346)
IEEE DOI
2106
Training, Image segmentation, Visualization, Protocols, Annotations,
Histopathology, Training data
BibRef
Gong, X.[Xuan],
Chen, S.[Shuyan],
Zhang, B.C.[Bao-Chang],
Doermann, D.[David],
Style Consistent Image Generation for Nuclei Instance Segmentation,
WACV21(3993-4002)
IEEE DOI
2106
Training, Image segmentation, Image analysis, Histopathology, Shape,
Image synthesis, Pipelines
BibRef
Zhao, S.[Shuai],
Li, X.[Xuanya],
Chen, Z.N.[Zhi-Neng],
Liu, C.[Chang],
Peng, C.G.[Chang-Gen],
Res2-unet: An Enhanced Network for Generalized Nuclear Segmentation in
Pathological Images,
MMMod21(II:87-98).
Springer DOI
2106
BibRef
Luo, J.Q.[Jia-Qi],
Zhao, Z.C.[Zhi-Cheng],
Su, F.[Fei],
Guo, L.[Limei],
Triplet-path Dilated Network for Detection and Segmentation of
General Pathological Images,
ICPR21(1452-1459)
IEEE DOI
2105
Image segmentation, Pathology, Visualization,
Object detection, Feature extraction, Robustness
BibRef
Yao, Z.Y.[Ze-Yi],
Li, K.Q.[Kai-Qi],
Luo, Y.[Yiwen],
Zhou, X.G.[Xiao-Guang],
Sun, M.[Muyi],
Zhang, G.H.[Guan-Hong],
Accurate Cell Segmentation in Digital Pathology Images via Attention
Enforced Networks,
ICPR21(1590-1595)
IEEE DOI
2105
Pathology, Image segmentation, Solid modeling, Design automation,
Image color analysis, Pipelines, Prediction algorithms,
digital pathology images
BibRef
Shin, B.[Beomjo],
Cho, J.[Junsu],
Yu, H.[Hwanjo],
Choi, S.J.[Seung-Jin],
Sparse Network Inversion for Key Instance Detection in Multiple
Instance Learning,
ICPR21(4083-4090)
IEEE DOI
2105
Training, Gradient methods, Histopathology, Neural networks,
Predictive models, Pattern recognition, Numerical models
BibRef
Ozen, Y.[Yigit],
Aksoy, S.[Selim],
Kösemehmetoglu, K.[Kemal],
Önder, S.[Sevgen],
Üner, A.[Aysegül],
Self-Supervised Learning with Graph Neural Networks for Region of
Interest Retrieval in Histopathology,
ICPR21(6329-6334)
IEEE DOI
2105
Training, Learning systems, Histopathology, Shape, Transfer learning,
Image retrieval, Breast, Digital pathology,
content-based image retrieval
BibRef
Sikaroudi, M.[Milad],
Ghojogh, B.[Benyamin],
Karray, F.[Fakhri],
Crowley, M.[Mark],
Tizhoosh, H.R.,
Batch-Incremental Triplet Sampling for Training Triplet Networks
Using Bayesian Updating Theorem,
ICPR21(7080-7086)
IEEE DOI
2105
Training, Histopathology, Training data, Stochastic processes,
Gaussian distribution, Bayes methods, Data mining
BibRef
Bussola, N.[Nicole],
Marcolini, A.[Alessia],
Maggio, V.[Valerio],
Jurman, G.[Giuseppe],
Furlanello, C.[Cesare],
AI Slipping on Tiles: Data Leakage in Digital Pathology,
AIDP20(167-182).
Springer DOI
2103
Reproducible results.
BibRef
Sikaroudi, M.[Milad],
Ghojogh, B.[Benyamin],
Safarpoor, A.[Amir],
Karray, F.[Fakhri],
Crowley, M.[Mark],
Tizhoosh, H.R.[Hamid R.],
Offline Versus Online Triplet Mining Based on Extreme Distances of
Histopathology Patches,
ISVC20(I:333-345).
Springer DOI
2103
BibRef
Maleki, D.[Danial],
Afshari, M.[Mehdi],
Babaie, M.[Morteza],
Tizhoosh, H.R.,
Ink Marker Segmentation in Histopathology Images Using Deep Learning,
ISVC20(I:359-368).
Springer DOI
2103
BibRef
Cheng, H.T.[Hsien-Tzu],
Yeh, C.F.[Chun-Fu],
Kuo, P.C.[Po-Chen],
Wei, A.[Andy],
Liu, K.C.[Keng-Chi],
Ko, M.C.[Mong-Chi],
Chao, K.H.[Kuan-Hua],
Peng, Y.C.[Yu-Ching],
Liu, T.L.[Tyng-Luh],
Self-similarity Student for Partial Label Histopathology Image
Segmentation,
ECCV20(XXV:117-132).
Springer DOI
2011
BibRef
Xiang, Y.,
Chen, J.,
Liu, Q.,
Liang, Y.,
Disentangled Representation Learning Based Multidomain Stain
Normalization For Histological Images,
ICIP20(360-364)
IEEE DOI
2011
Image color analysis, Image reconstruction,
Generative adversarial networks, Training, Decoding, Generators,
Deep Learning
BibRef
Hosseini, M.S.[Mahdi S.],
Chan, L.[Lyndon],
Huang, W.M.[Wei-Min],
Wang, Y.C.[Yi-Chen],
Hasan, D.[Danial],
Rowsell, C.[Corwyn],
Damaskinos, S.[Savvas],
Plataniotis, K.N.[Konstantinos N.],
On Transferability of Histological Tissue Labels in Computational
Pathology,
ECCV20(XXIX: 453-469).
Springer DOI
2010
BibRef
Cheeseman, A.K.[Alison K.],
Tizhoosh, H.R.[Hamid R.],
Vrscay, E.R.[Edward R.],
Studying the Effect of Digital Stain Separation of Histopathology
Images on Image Search Performance,
ICIAR20(II:262-273).
Springer DOI
2007
BibRef
Alinsaif, S.,
Lang, J.,
Histological Image Classification using Deep Features and Transfer
Learning,
CRV20(101-108)
IEEE DOI
2006
Deep learning, Fine-tuning, CNN-Based Features, histopathological,
SVM, classification
BibRef
Hosseini, M.S.[Mahdi S.],
Chan, L.[Lyndon],
Tse, G.[Gabriel],
Tang, M.[Michael],
Deng, J.[Jun],
Norouzi, S.[Sajad],
Rowsell, C.[Corwyn],
Plataniotis, K.N.[Konstantinos N.],
Damaskinos, S.[Savvas],
Atlas of Digital Pathology: A Generalized Hierarchical Histological
Tissue Type-Annotated Database for Deep Learning,
CVPR19(11739-11748).
IEEE DOI
2002
BibRef
Hou, L.[Le],
Agarwal, A.[Ayush],
Samaras, D.[Dimitris],
Kurc, T.M.[Tahsin M.],
Gupta, R.R.[Rajarsi R.],
Saltz, J.H.[Joel H.],
Robust Histopathology Image Analysis: To Label or to Synthesize?,
CVPR19(8525-8534).
IEEE DOI
2002
BibRef
Cheeseman, A.K.[Alison K.],
Tizhoosh, H.[Hamid],
Vrscay, E.R.[Edward R.],
A Compact Representation of Histopathology Images Using Digital Stain
Separation and Frequency-Based Encoded Local Projections,
ICIAR19(II:147-158).
Springer DOI
1909
BibRef
Stanisavljevic, M.[Milos],
Anghel, A.[Andreea],
Papandreou, N.[Nikolaos],
Andani, S.[Sonali],
Pati, P.[Pushpak],
Rüschoff, J.H.[Jan Hendrik],
Wild, P.[Peter],
Gabrani, M.[Maria],
Pozidis, H.[Haralampos],
A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide
Images in Histopathology,
BioIm18(VI:424-436).
Springer DOI
1905
BibRef
Kieffer, B.,
Babaie, M.,
Kalra, S.,
Tizhoosh, H.R.,
Convolutional neural networks for histopathology image
classification: Training vs. Using pre-trained networks,
IPTA17(1-6)
IEEE DOI
1804
feature extraction, image classification, image representation,
learning (artificial intelligence), medical image processing,
medical imaging
BibRef
Valkonen, M.,
Kartasalo, K.,
Liimatainen, K.,
Nykter, M.,
Latonen, L.,
Ruusuvuori, P.,
Dual Structured Convolutional Neural Network with Feature
Augmentation for Quantitative Characterization of Tissue Histology,
BioIm17(27-35)
IEEE DOI
1802
Biological system modeling, Feature extraction, Histograms,
Image analysis, Pathology, Training
BibRef
Li, W.,
Qian, X.,
Ji, J.,
Noise-tolerant deep learning for histopathological image segmentation,
ICIP17(3075-3079)
IEEE DOI
1803
Diseases, Image color analysis, Image segmentation,
Machine learning, Muscles, Noise measurement, Training,
noisy labels
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Astola, L.[Laura],
Stain separation in digital bright field histopathology,
IPTA16(1-6)
IEEE DOI
1703
biological tissues
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Agarwal, N.[Nitin],
Xu, X.M.[Xiang-Min],
Gopi, M.,
Automatic Detection of Histological Artifacts in Mouse Brain Slice
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MCV16(105-115).
Springer DOI
1711
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Corredor, G.[German],
Romero, E.[Eduardo],
Learning histopathological regions of interest by fusing bottom-up
and top-down information,
ICIP15(3200-3204)
IEEE DOI
1512
Histopathology
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Li, X.Y.[Xing-Yu],
Plataniotis, K.N.[Konstantinos N.],
Diagnostic color estimation of tissue components in pathology images
via von Mises mixture model,
ICIP15(2060-2064)
IEEE DOI
1512
Pathology image
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Hatipoglu, N.,
Bilgin, G.,
Classification of histopathological images using convolutional neural
network,
IPTA14(1-6)
IEEE DOI
1503
image classification
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McCann, M.T.[Michael T.],
Majumdar, J.[Joshita],
Peng, C.[Cheng],
Castro, C.A.[Carlos A.],
Kovacevic, J.[Jelena],
Algorithm and benchmark dataset for stain separation in histology
images,
ICIP14(3953-3957)
IEEE DOI
1502
Accuracy
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Sommer, C.[Christoph],
Fiaschi, L.[Luca],
Hamprecht, F.A.[Fred A.],
Gerlich, D.W.[Daniel W.],
Learning-based mitotic cell detection in histopathological images,
ICPR12(2306-2309).
WWW Link.
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Toutain, M.,
Lézoray, O.,
Audigié, F.,
Busoni, V.,
Rossi, G.,
Parillo, F.,
El Moataz, A.,
Analysis of Whole Slide Images of Equine Tendinopathy,
ICIAR12(II: 440-447).
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1206
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Díaz, G.[Gloria],
Romero, E.[Eduardo],
Histopathological Image Classification Using Stain Component Features
on a pLSA Model,
CIARP10(55-62).
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1011
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Cooper, L.[Lee],
Saltz, J.[Joel],
Machiraju, R.[Raghu],
Huang, K.[Kun],
Two-point correlation as a feature for histology images:
Feature space structure and correlation updating,
MMBIA10(79-86).
IEEE DOI
1006
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Graf, F.[Felix],
Grzegorzek, M.[Marcin],
Paulus, D.[Dietrich],
Counting Lymphocytes in Histopathology Images Using Connected
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ICPR-Contests10(263-269).
Springer DOI
1008
BibRef
Cheng, J.[Jierong],
Veronika, M.[Merlin],
Rajapakse, J.C.[Jagath C.],
Identifying Cells in Histopathological Images,
ICPR-Contests10(244-252).
Springer DOI
1008
BibRef
Kuse, M.[Manohar],
Sharma, T.[Tanuj],
Gupta, S.[Sudhir],
A Classification Scheme for Lymphocyte Segmentation in H&E Stained
Histology Images,
ICPR-Contests10(235-243).
Springer DOI
1008
BibRef
Gurcan, M.N.[Metin N.],
Madabhushi, A.[Anant],
Rajpoot, N.[Nasir],
Pattern Recognition in Histopathological Images: An ICPR 2010 Contest,
ICPR-Contests10(226-234).
Springer DOI
1008
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Thomas, K.A.[Kristine A.],
Sottile, M.J.[Matthew J.],
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Unsupervised Segmentation for Inflammation Detection in Histopathology
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ICISP10(541-549).
Springer DOI
1006
BibRef
Noah, S.A.[Shahrul Azman],
Yaakob, S.[Suraya],
Shahar, S.[Suzana],
Application of Information Visualization Techniques in Representing
Patients' Temporal Personal History Data,
IVIC09(168-179).
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0911
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Miller, M.[Matt],
Graf, H.P.[Hans Peter],
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Grading nuclear pleomorphism on histological micrographs,
ICPR08(1-4).
IEEE DOI
0812
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Thomas, G.K.[Georgia K.],
Cheng, K.C.[Keith C.],
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Liu, Y.X.[Yan-Xi],
Automatic lattice detection in near-regular histology array images,
ICIP08(1452-1455).
IEEE DOI
0810
BibRef
And:
Towards efficient automated characterization of irregular histology
images via transformation to frieze-like patterns,
CIVR08(581-590).
0807
BibRef
Zhao, D.H.[De-Hua],
Chen, Y.X.[Yi-Xin],
Correa, H.,
Statistical Categorization of Human Histological Images,
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IEEE DOI
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An evolutionary snake algorithm for the segmentation of nuclei in
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0505
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Nedzved, A.,
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Morphological Segmentation of Histology Cell Images,
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IEEE DOI
0009
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Fluorescence Analysis, Microscopic Analysis, Cells .