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Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review

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Histopathology image classification: highlighting the gap between manual analysis and AI automation

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Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning

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Tumor circulating biomarkers in colorectal cancer

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Context aware machine learning techniques for brain tumor classification and detection – A review

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Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects

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Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images

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Digital Technology: Key considerations for the use of artificial intelligence in healthcare and clinical research

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Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology

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Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach

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Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images

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A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

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A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer

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Decoding pathology: the role of computational pathology in research and diagnostics

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Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer

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The value of the tumour-stroma ratio for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer: a case control study

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AI Model for Prostate Biopsies Predicts Cancer Survival

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Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning

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High-multiplex tissue imaging in routine pathology—are we there yet?

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Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

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A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical microscopy images

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Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

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Offline Versus Online Triplet Mining Based on Extreme Distances of Histopathology Patches

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Identifying gross post‐mortem organ images using a pre‐trained convolutional neural network

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Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review

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FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

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Continual learning in medical image analysis: A survey

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Perspectives in pathomics in head and neck cancer

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Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images

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Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention

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Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

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Artificial Intelligence in Colorectal Cancer: From Patient Screening over Tailoring Treatment Decisions to Identification of Novel Biomarkers

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Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology

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Systematic comparison of semi-supervised and self-supervised learning for medical image classification

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A Deformable CRF Model for Histopathology Whole-Slide Image Classification

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Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

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Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology

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Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

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Identify Representative Samples by Conditional Random Field of Cancer Histology Images

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TransNetV: An optimized hybrid model for enhanced colorectal cancer image classification

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

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Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning

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State of machine and deep learning in histopathological applications in digestive diseases

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A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer

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A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer

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Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI

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Galactic swarm optimization with deep transfer learning driven colorectal cancer classification for image guided intervention

Escorcia‐Gutierrez, J., Gamarra, M., Ariza-Colpas, P., Roncallo, G. Y. B., Leal, N., Soto-Díaz, R., & Mansour, R. F. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108462 · Cited by 19

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

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MonoNet: enhancing interpretability in neural networks via monotonic features

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A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

Li, Y., Chou, H., Lin, P., Shen, M., & Hsieh, S. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04530-8 · Cited by 18

Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma

Duan, L., He, Y., Guo, W., Du, Y., Yin, S., Yang, S., Dong, G., Li, W., & Chen, F. (2024) · Journal of Neuro-Oncology · doi:10.1007/s11060-024-04665-8 · Cited by 20

Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks

Garland, J., Hu, M., Duffy, M., Kesha, K., Glenn, C., Morrow, P. L., Stables, S. R., Ondruschka, B., Broi, U. D., & Tse, R. D. (2021) · American Journal of Forensic Medicine & Pathology · doi:10.1097/paf.0000000000000672 · Cited by 18

A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI

Lou, S., Ji, J., Li, H., Zhang, X., Jiang, Y., Hua, M., Chen, K., Ge, K., Zhang, Q., Wang, L., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04489-9 · Cited by 18

Multimodal Large Language Models are Generalist Medical Image Interpreters

Han, T., Adams, L. C., Nebelung, S., Kather, J. N., Bressem, K. K., & Truhn, D. (2023) · doi:10.1101/2023.12.21.23300146 · Cited by 18

Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma

Kronberg, R. M., Häberle, L., Pfaus, M., Xu, H. C., Krings, K. S., Schlensog, M., Rau, T. T., Pandyra, A. A., Lang, K. S., Esposito, I., et al. (2022) · Cancers · doi:10.3390/cancers14081964 · Cited by 18

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer

Joshi, P., & Dhar, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18874-6 · Cited by 20

Senescence‐based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies

Lv, M., Cai, D., Li, C., Chen, J., Li, G., Hu, C., Gai, B., Lei, J., Lan, P., Wu, X., et al. (2023) · MedComm · doi:10.1002/mco2.333 · Cited by 17

Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis

Prezja, F., Annala, L., Kiiskinen, S., & Ojala, T. (2023) · Algorithms · doi:10.3390/a17010008 · Cited by 18

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Vray, G., Tomar, D., Bozorgtabar, B., & Thiran, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3355645 · Cited by 19

Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia

Viet, C. T., Zhang, M., Dharmaraj, N., Li, G. Y., Pearson, A. T., Mañón, V. A., Grandhi, A., Xu, K., Aouizerat, B. E., & Young, S. W. (2024) · Tissue Engineering Part A · doi:10.1089/ten.tea.2024.0096 · Cited by 17

Colorectal Cancer Tissue Classification Using Semi-Supervised Hypergraph Convolutional Network

Bakht, A. B., Javed, S., Al-Marzouqi, H., Khandoker, A. H., & Werghi, N. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434036 · Cited by 17

Colon Cancer Classification of Histopathological Images Using Data Augmentation

Kumar, A., Vishwakarma, A., Bajaj, V., Sharma, A. K., & Thakur, C. (2021) · International Conference on Control, Automation, Power and Signal Processing (CAPS) · doi:10.1109/caps52117.2021.9730704 · Cited by 17

Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net

Xu, J., Cai, C., Zhou, Y., Yao, B., Xu, G., Wang, X., Zhao, K., Madabhushi, A., Liu, Z., & Li, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23937-4_12 · Cited by 17

Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural NetworkPreprint

Al.Shawesh, R., & Chen, Y. X. (2021) · medRxiv · doi:10.1101/2021.03.17.21253390 · Cited by 17

Converging deep learning and human-observed tumor-adipocyte interaction as a biomarker in colorectal cancer

Reitsam, N. G., Grosser, B., Steiner, D. F., Grozdanov, V., Wulczyn, E., L’Imperio, V., Plass, M., Müller, H., Zatloukal, K., Muti, H. S., et al. (2024) · Communications Medicine · doi:10.1038/s43856-024-00589-6 · Cited by 16

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Lapuente-Santana, Ó., Kant, J., & Eduati, F. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00749-w · Cited by 17

Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging

Keogan, A., Nguyen, T. N. Q., Bouzy, P., Stone, N., Jirström, K., Rahman, A., Gallagher, W. M., & Meade, A. D. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00772-x · Cited by 17

A Deep Neural Network–Based Decision Support Tool for the Detection of Lymph Node Metastases in Colorectal Cancer Specimens

Kindler, C., Elfwing, S., Öhrvik, J., & Nikberg, M. (2023) · Modern Pathology · doi:10.1016/j.modpat.2022.100015 · Cited by 15

Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E‐stained tissues

Shen, B., Saito, A., Ueda, A., Fujita, K., Nagamatsu, Y., Hashimoto, M., Kobayashi, M., Mirza, A. H., Graf, H. P., Cosatto, E., et al. (2023) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.314 · Cited by 16

Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree

Liang, M., Wang, R., Liang, J., Wang, L., Li, B., Jia, X., Zhang, Y., Chen, Q., Zhang, T., & Zhang, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3326467 · Cited by 15

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images

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Adaptive Dynamic Learning Rate Optimization Technique for Colorectal Cancer Diagnosis Based on Histopathological Image Using EfficientNet-B0 Deep Learning Model

El-Ghany, S. A., Mahmood, M. A., & El-Aziz, A. A. A. (2024) · Electronics · doi:10.3390/electronics13163126 · Cited by 16

Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image

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Integrating Machine Learning and Tumor Immune Signature to Predict Oncologic Outcomes in Resected Biliary Tract Cancer

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