Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

Deep Residual Learning for Image Recognition

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ImageNet classification with deep convolutional neural networks

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Going deeper with convolutions

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Xception: Deep Learning with Depthwise Separable Convolutions

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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

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Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

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Taskonomy: Disentangling Task Transfer Learning

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Classification of breast cancer histology images using Convolutional Neural Networks

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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

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Graph Neural Networks and Their Current Applications in Bioinformatics

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Artificial intelligence for digital and computational pathology

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Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study

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A comprehensive review of deep learning in colon cancer

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Vision Transformers in medical computer vision—A contemplative retrospection

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CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

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Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology

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Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification

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Hierarchical graph representations in digital pathology

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One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification

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A survey on graph-based deep learning for computational histopathology

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Vision Transformers for Computational Histopathology

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Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

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Computational pathology: A survey review and the way forward

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Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

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A novel transfer learning approach for the classification of histological images of colorectal cancer

Ohata, E. F., das Chagas, J. V. S., Bezerra, G. M., Hassan, M. M., de Albuquerque, V. H. C., & Filho, P. P. R. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03575-6 · Cited by 91

Deep learning techniques for tumor segmentation: a review

Jiang, H., Diao, Z., & Yao, Y. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03901-6 · Cited by 86

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

Graham, S., Epstein, D. B. A., & Rajpoot, N. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3013246 · Cited by 87

A survey on artificial intelligence in histopathology image analysis

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A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images

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Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

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CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Graham, S., Vu, Q. D., Jahanifar, M., WEIGERT, M. G., Schmidt, U., Zhang, W., Zhang, J., Yang, S., Xiang, J., Wang, X., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103047 · Cited by 63

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning

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Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

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HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism

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Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

Chlorogiannis, D., Verras, G., Tzelepi, V., Chlorogiannis, A., Apostolos, A., Kotis, K., Anagnostopoulos, C., Antzoulas, A., Vailas, M. G., Schizas, D., et al. (2023) · Gastroenterology Review · doi:10.5114/pg.2023.130337 · Cited by 65

Searching Images for Consensus

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Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

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CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

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Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials

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HistoSeg: Quick attention with multi-loss function for multi-structure segmentation in digital histology images

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An end-to-end breast tumour classification model using context-based patch modelling – A BiLSTM approach for image classification

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Unleashing the potential of AI for pathology: challenges and recommendations

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Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

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An aggregation of aggregation methods in computational pathology

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Generating and evaluating synthetic data in digital pathology through diffusion models

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Learning Whole-Slide Segmentation from Inexact and Incomplete Labels Using Tissue Graphs

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Deep Learning Applications in Medical Image Analysis

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Knowledge distillation and teacher–student learning in medical imaging: Comprehensive overview, pivotal role, and future directions

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Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images

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Clinical applications of graph neural networks in computational histopathology: A review

Meng, X., & Zou, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107201 · Cited by 42

Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

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A digital score of tumour‐associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

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Nuclei and glands instance segmentation in histology images: a narrative review

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A digital score of peri‐epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia

Bashir, R. M. S., Shephard, A., Mahmood, H., Azarmehr, N., Raza, S. E. A., Khurram, S. A., & Rajpoot, N. (2023) · The Journal of Pathology · doi:10.1002/path.6094 · Cited by 38

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence

Gabralla, L. A., Hussien, A. M., AlMohimeed, A., Saleh, H., Alsekait, D. M., El–Sappagh, S., Ali, A. A., & Hassan, M. R. (2023) · Diagnostics · doi:10.3390/diagnostics13182939 · Cited by 35

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality

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HistoCartography: A Toolkit for Graph Analytics in Digital PathologyPreprint

Jaume, G., Pati, P., Anklin, V., Foncubierta–Rodríguez, A., & Gabrani, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10073 · Cited by 35

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy, Z., Abdelsamea, M. M., Mohamed, M. M., & Gaber, M. M. (2021) · Entropy · doi:10.3390/e23050620 · Cited by 34

SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images

Deshpande, S., Minhas, F., Graham, S., & Rajpoot, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102337 · Cited by 38

Joint categorical and ordinal learning for cancer grading in pathology images

Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102206 · Cited by 34

CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification

Wang, H., Huang, G., Zhao, Z., Liang Cheng, C., Juncker‐Jensen, A., Nagy, M. L., Lü, X., Zhang, X., & Chen, D. Z. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3249343 · Cited by 30

Enhancing generalizability of data-driven urban flood models by incorporating contextual information

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Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images

Dogar, G. M., Shahzad, M., & Fraz, M. M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104199 · Cited by 30

Bayesian Collaborative Learning for Whole-Slide Image Classification

Yu, J.-G., Wu, Z., Ming, Y., Deng, S., Wu, Q., Xiong, Z., Yu, T., Xia, G.-S., Jiang, Q., & Li, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3241204 · Cited by 29

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification

Li, Y., Shen, Y., Zhang, J., Song, S., Li, Z., Ke, J., & Shen, D. G. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3317132 · Cited by 28

Knowledge Distillation in Histology Landscape by Multi-Layer Features Supervision

Javed, S., Mahmood, A., Qaiser, T., & Werghi, N. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3237749 · Cited by 24

Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network

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Hybrid Supervision Learning for Pathology Whole Slide Image Classification

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Polyp Segmentation via Semantic Enhanced Perceptual Network

Wang, T., Qi, X., & Yang, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3432882 · Cited by 21

A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification

Bai, Y., Mi, Y., Su, Y., Zhang, B., Zhang, Z., Wu, J., Huang, H., Xiong, Y., Gong, X., & Wang, W. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199110 · Cited by 20

A Federated Learning Approach to Tumor Detection in Colon Histology Images

Gunesli, G. N., Bilal, M., Raza, S. E. A., & Rajpoot, N. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01994-5 · Cited by 20

A Multimodal Transfer Learning Approach Using PubMedCLIP for Medical Image Classification

Dao, H. N., Nguyen, T. D., Mugisha, C., & Paik, I. (2024) · IEEE Access · doi:10.1109/access.2024.3401777 · Cited by 19

DONet: Dual Objective Networks for Skin Lesion SegmentationPreprint

Wang, Y., Wei, Y., Qian, X., Zhu, L., & Yang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08278 · Cited by 24

CaMeL-Net: Centroid-aware metric learning for efficient multi-class cancer classification in pathology images

Lee, J., Han, C., Kim, K., Park, G.-H., & Kwak, J. T. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107749 · Cited by 16

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma

Roetzer-Pejrimovsky, T., Nenning, K., Kiesel, B., Klughammer, J., Rajchl, M., Baumann, B., Langs, G., & Wöehrer, A. (2024) · GigaScience · doi:10.1093/gigascience/giae057 · Cited by 16

Deep Multi-Resolution Dictionary Learning for Histopathology Image AnalysisPreprint

Hatami, N., Bilal, M., & Rajpoot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00669 · Cited by 15

An unsupervised transfer learning model based on convolutional auto encoder for non-alcoholic steatohepatitis activity scoring and fibrosis staging of liver histopathological images

Karagöz, M. A., Akay, B., Baştürk, A., Karaboğa, D., & Nalbantoğlu, Ö. U. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08252-2 · Cited by 14

Review of deep learning-based pathological image classification: From task-specific models to foundation models

Luan, H., Yang, K., Hu, T., Hu, J., Liu, S., Li, R., He, J., Yan, R., Guo, X., Qian, N., et al. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107578 · Cited by 15

Multi-scale Multi-instance Multi-feature Joint Learning Broad Network (M3JLBN) for gastric intestinal metaplasia subtype classification

Lai, Q., Vong, C., Wong, P. K., Wang, S. T., Yan, T., Choi, I. C., & Yu, H. H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108960 · Cited by 15

Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer

Lan, J., Chen, M., Wang, J., Du, M., Wu, Z., Zhang, H., Xue, Y., Wang, T., Chen, L., Xu, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101004 · Cited by 16

Hepatocellular carcinoma histopathological images grading with a novel attention-sharing hybrid network based on multi-feature fusion

Zhang, J., Qiu, S., Li, Q., Zhou, C., Hu, Z., Weng, J., Sheng, X., Dong, Q., & Ren, N. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105126 · Cited by 14

Multiscale Dilated UNet for Segmentation of Multi-Organ Nuclei in Digital Histology Images

Rashid, S. N., Fraz, M. M., & Javed, S. (2020) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet50430.2020.9322833 · Cited by 14

Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking

Kumar, A., Vishwakarma, A., & Bajaj, V. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18357-y · Cited by 14

Knowledge-Enhanced Visual-Language Pretraining for Computational Pathology

Zhou, X., Zhang, X., Wu, C., Zhang, Y., Xie, W., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72943-0_20 · Cited by 20

FedDropoutAvg: Generalizable federated learning for histopathology image classificationPreprint

Gunesli, G. N., Bilal, M., Raza, S. E. A., & Rajpoot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13230 · Cited by 13

Knowledge-enhanced pretraining for vision-language pathology foundation model on cancer diagnosis

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