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Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning

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FLAME: Federated Learning With Masked Autoencoders and Mean-Prototypes Embedding for Sparsely Labeled Medical Images

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TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

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Cancer prediction using RNA sequencing and deep learning

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Value of the Tumor-Stroma Ratio and Structural Heterogeneity Measured by a Novel Semiautomatic Image Analysis Technique for Predicting Survival in Patients With Colon Cancer

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Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis

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OpenSlideFM: A Computationally Efficient Multi-Scale Foundation Model for Computational PathologyPreprint

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Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem

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Medical images under tampering

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Complex wound analysis using AI

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Automated Detection of Kaposi Sarcoma–Associated Herpesvirus–Infected Cells in Immunohistochemical Images of Skin Biopsies

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HLIP: A pan-cancer model for histological image analysis in clinical research using TCGA

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Repositioning Proton Pump Inhibitors: Expanding Applications from Acid Suppression to Cancer Therapeutics

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FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation

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MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI

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Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients

Wu, L., Jin, Y., Hao, K., & Yu, H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209071 · Cited by 2

Colorectal disease diagnosis with deep triple-stream fusion and attention refinement

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Colorectal cancer worldwide: epidemiological trends, economic burden, and the promise of AI-driven solutions

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Automated Pathomic Analysis of Angiogenesis and Immune Profiles Unveils an Interpretable Prognostic Biomarker in Colon and Gastric Cancers

Prunella, M., Altini, N., D’Alessandro, R., Schirizzi, A., Leonardis, G. D., Arborea, G., Savino, M. T., Valentini, A. M., Armentano, R., Ricci, A. D., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3625431 · Cited by 2

Conservative-Radical Complementary Learning for Class-Incremental Medical Image Analysis with Pre-trained Foundation Models

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A histopathology aware DINO model with attention based representation enhancement

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Exploring the molecular intersections of osteoporosis and sarcopenia: An integrated bioinformatics and experimental validation

Lv, Y., Du, Y., Lin, P., Liu, J., Pu, S., & Lu, S. (2026) · Experimental Gerontology · doi:10.1016/j.exger.2026.113033 · Cited by 2

Integrating artificial intelligence ( AI ) into colorectal cancer reporting

Bräutigam, K., Baker, A., Koelzer, V. H., Kather, J. N., & Graham, T. A. (2026) · The Journal of Pathology · doi:10.1002/path.70029 · Cited by 2

Two decades of artificial intelligence in colon cancer diagnosis and treatment: a bibliometric analysis of research trends (2003–2023)

Wu, C., Zhong, W.-J., Wu, S.-Z., & Ye, K. (2026) · Updates in Surgery · doi:10.1007/s13304-026-02533-w · Cited by 2

Attention-Driven Feature Extraction for XAI in Histopathology Leveraging a Hybrid Xception Architecture for Multi-Cancer Diagnosis

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Prototype-driven expert voting for pseudo-label selection with dual-balanced loss for imbalanced medical image classification

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Multimodal dynamic fusion framework for survival prediction in clear cell renal cell carcinoma

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Antibody–drug conjugates in colorectal cancer: advances in targeted delivery and personalized oncology

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GEM-Net: a compact Ghost-ECA CNN with multi-stage feature fusion for multi-domain histopathology classification

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Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer

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Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application

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Leveraging Multi-Model Machine Learning Algorithms for Tumor–Normal Classification and Discovery of Biomarkers in Colorectal Cancer Using Multi-Omics Data

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EQUI-ViT: Rotational Equivariant Vision Transformer for Robust Histopathology Analysis

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Advanced deep learning applications in diagnostic pathology

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Application of an indocyanine green-mediated fluorescence imaging navigation system in detecting mice tumors

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Transfer learning based classification of MSI and MSS gastrointestinal cancer

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Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence

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Role of AI-Based Methods in Colorectal Cancer Diagnostics

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Fuzzy Metadata Augmentation for Multimodal Data Classification

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Ensemble Knowledge Distillation for Edge Intelligence in Medical Applications

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A generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split

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Machine learning algorithms-based decision support model for diabetes

Karthick, K., Manikandan, R., & Kumar, T. S. (2024) · Review of Computer Engineering Research · doi:10.18488/76.v11i1.3598 · Cited by 3

MiniDigPath: A New Standard For Pathology Images Few-Shot Learning Classification

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E-MedViTR: Enhanced Vision Transformers with Registers for Biomedical Image Classification

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Histology Image Artifact Restoration with Lightweight Transformer Based Diffusion Model

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Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction

Adjei, P. E., Otoo, O. K., Yang, J., & Rao, N. (2024) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3673971.3673993 · Cited by 2

Wavelet-Based Prototype Learning for Medical Image Classification

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Nuclear morphology explained through digital morphometry: differentiating nuclear features across the three histological grades in cutaneous squamous cell carcinoma

Buruiană, A., Şerbănescu, M.-S., Pop, B., Gheban, B., Gheban-Roşca, I. A., Georgiu, C., Crişan, D., & Crişan, M. (2024) · Romanian Journal of Morphology and Embryology · doi:10.47162/rjme.65.3.04 · Cited by 2

CLOG-CD: Curriculum Learning Based on Oscillating Granularity of Class Decomposed Medical Image Classification

Abbas, A., Gaber, M. M., & Abdelsamea, M. M. (2025) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2025.3562620 · Cited by 2

Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization

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Debunking Optimization Myths in Federated Learning for Medical Image Classification

Lee, Y., Lee, H., Gong, J., Cao, Y., & Kang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13961-0_33 · Cited by 3

A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection

Nasr, M., Abdelhamid, L., & Shehata, N. S. (2020) · Future Computing and Informatics Journal · doi:10.54623/fue.fcij.4.2.6 · Cited by 2

Artificial intelligence in pathological evaluation of gastrointestinal cancers

Alpsoy, A., Yavuz, A., & Elpek, G. Ö. (2021) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v2.i6.141 · Cited by 2

Radiomics and Radiogenomics

Li, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-83047-2_16 · Cited by 2

Antifibrotic effects of Quercetin on TGF-β1-induced vocal fold fibroblasts.

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Biomedical evaluation of green tea herbal liquid with bama miniature pigs

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Diagnosis and survival prediction of colorectal cancer using deep learning from the histopathological HE images

Zhu, X., Hu, S., Wu, J., Zhang, Y., Zheng, D., Zhang, S., & Yang, H. (2023) · Optoelectronic Imaging and Multimedia Technology · doi:10.1117/12.2687606 · Cited by 2

A unique hybrid domain hand-crafted feature to classify colorectal tissue histopathological images using multiheaded CNN

KUMAR, A., Vishwakarma, A., & Bajaj, V. (2023) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4030 · Cited by 2

Automated Diagnosis of Melanoma Histopathological Images Based on Deep Learning Using Trust Counting Method

Xie, P., Li, T., Li, F., Liu, J., Zhou, J., & Zuo, K. (2021) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic54370.2021.9647055 · Cited by 2

C2G-Net: Exploiting Morphological Properties for Image ClassificationPreprint

Herbsthofer, L., Prietl, B., Tomberger, M., Pieber, T. R., & López-García, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03378 · Cited by 2

EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification BoostingPreprint

Hassan, M., Xie, C., Sigel, C., Doukas, M., Alpert, L., Jarnagin, W. R., Simpson, A., & Fuchs, T. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.11085 · Cited by 2

Out of distribution detection for skin and malaria imagesPreprint

Zaida, M., Ali, S., Ali, M., Hussein, S., Saadia, A., & Sultani, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01505 · Cited by 2

A deep learning and graph-based approach to characterise the immunological landscape and spatial architecture of colon cancer tissuePreprint

Parreno-Centeno, M., Tagliazucchi, G. M., Withnell, E., Shi, P., & Secrier, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.06.498984 · Cited by 2

Histolab: A Python Library for Reproducible Digital Pathology Preprocessing with Automated TestingPreprint

Marcolini, A., Bussola, N., Arbitrio, E., Amgad, M. T., Jurman, G., & Furlanello, C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4127825 · Cited by 2

Screening of normal endoscopic large bowel biopsies with artificial intelligence: a retrospective studyPreprint

Graham, S., Minhas, F., Bilal, M., Ali, M., Tsang, Y. W., Eastwood, M., Wahab, N., Jahanifar, M., Hero, E., Dodd, K., et al. (2022) · medRxiv · doi:10.1101/2022.10.17.22279804 · Cited by 2

A Novel Skin Cancer Assisted Diagnosis Method based on Capsule Networks with CBAMPreprint

Lan, Z., Cai, S., Zhu, J., & Xu, Y. (2023) · doi:10.36227/techrxiv.23291003 · Cited by 3

A Pathology-Interpretable Deep Learning Model for Predicting Microsatellite Instability State in Colorectal Cancer: Validation across Diverse Platforms and Asian CohortsPreprint

Zhang, Z., Wang, W., Song, Y., Liu, X., Yang, P., Shi, H., Tian, G., Yang, J., & Xing, X. (2024) · Research Square · doi:10.21203/rs.3.rs-3874988/v1 · Cited by 2

I-Viewer: An Online Digital Pathology Analysis Platform with Agentic-RAG AI CopilotPreprint

Rong, R., Luo, D., Gu, Z., Quan, P., Villanueva-Miranda, I., Wang, J., Yang, S., Chi, Z., Leavey, P. J., Yang, D. M., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-5404747/v1 · Cited by 2

VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression

Doğan, R. S., Akay, E., Doğan, S., & Yılmaz, B. (2025) · Scientific Data · doi:10.1038/s41597-025-06168-1 · Cited by 2

Identifying novel prognostic markers and genotype-phenotype associations in endometrioid endometrial carcinoma by computational analysis of histopathological images

Cheng, J., Liu, Y., Huang, W., Hong, W., Wang, L., & Ni, D. (2021) · Medicine in Omics · doi:10.1016/j.meomic.2021.100005 · Cited by 1

Understanding the impact of image and input resolution on deep digital pathology patch classifiers

Teh, E. W., & Taylor, G. W. (2022) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv55824.2022.00028 · Cited by 1

Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data

Herbsthofer, L., Tomberger, M., Smolle, M. A., Prietl, B., Pieber, T. R., & López-García, P. (2022) · Journal of medical imaging · doi:10.1117/1.jmi.9.6.067501 · Cited by 1

Deep Learning-Based Multi-state Colorectal Cancer Histological Image Classification

Kaur, H., Kumar, A., & Attri, V. K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-7077-3_39 · Cited by 1

New advances in artificial intelligence for the diagnosis and treatment of colorectal cancer: a literature review

Quinte, R. J. R., Osorio, A. B. O., Toapanta, C. D. T., Faican, E. A. L., & Toala, J. A. G. (2024) · Sapienza International Journal of Interdisciplinary Studies · doi:10.51798/sijis.v5i1.733 · Cited by 1

Non-negative subspace feature representation for few-shot learning in medical imaging

Fan, K., Cai, X., & Niranjan, M. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105334 · Cited by 1

Literature Review on Stages of Colorectal Cancer

Kalpana, K., & Babu, G. N. K. S. (2024) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst24116172 · Cited by 1

Domain-Adaptive Training for Security Health and Heterogeneously Medical Image

Chang, T., Cui, Y., Chen, Z., & Liang, X. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3514515 · Cited by 1

A comprehensive evaluation of data analysis approaches for predicting colorectal cancer

Rewatkar, R. M., Patil, A., & Bagde, A. (2024) · AIP conference proceedings · doi:10.1063/5.0244633 · Cited by 1

An Automated Histopathological Colorectal Cancer Multi‐Class Classification System Based on Optimal Image Processing and Prominent Features

Tonni, T. J., Rana, S., Fatema, K., Karim, A., Rony, M. A. H., Hasan, M. Z., Mukta, S. H., & Azam, S. (2024) · Computational Intelligence · doi:10.1111/coin.70007 · Cited by 1

Predicting ustekinumab treatment response in Crohn’s disease using pre-treatment biopsy images

Cai, C., Chen, R., Chen, J., Li, J., Lv, C., Jiao, Y., Wu, L., Chen, J., Sun, Q., Shi, Q., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf301 · Cited by 1

Deep Learning Can Accurately Predict the Prognosis of Gynecologic Smooth Muscle Tumors of Uncertain Malignant Potential: A Multicenter Pilot Study

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A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology

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