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Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions

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Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

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Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

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Nucleus-Aware Self-Supervised Pretraining Using Unpaired Image-to-Image Translation for Histopathology Images

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Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification

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Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

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An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

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

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RandStainNA++: Enhance Random Stain Augmentation and Normalization Through Foreground and Background Differentiation

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Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging

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Multi-resolution consistency semi-supervised active learning framework for histopathology image classification

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Knowledge-Enhanced Visual-Language Pretraining for Computational Pathology

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Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience

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Fused multi-level attention features with a constraint fusion network for colorectal tissue classification using histopathological images

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Identification of prognostic spatial organization features in colorectal cancer microenvironment using deep learning on histopathology images

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Histokt: Cross Knowledge Transfer in Computational Pathology

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Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in Histopathology

Scalbert, M., Vakalopoulou, M., & Couzinié-Devy, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_12 · Cited by 13

Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning

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Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

Huss, R., Raffler, J., & Märkl, B. (2023) · Cancer Reports · doi:10.1002/cnr2.1796 · Cited by 13

Self-distillation Augmented Masked Autoencoders for Histopathological Image Understanding

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Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

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The Crohn’s-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer

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Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual DictionaryPreprint

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Deep learning can predict multi-omic biomarkers from routine pathology images: A systematic large-scale studyPreprint

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

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Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

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Generous Approach for Diagnosis and Detection of Gastrointestinal Tract Disease with Application of Deep Neural Network

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From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images

Parvaiz, A., Nasir, E. S., & Fraz, M. M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01049-2 · Cited by 12

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding

Baheti, B., Innani, S., Nasrallah, M., & Bakas, S. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1304191 · Cited by 12

Prediction of prognosis using artificial intelligence‐based histopathological image analysis in patients with soft tissue sarcomas

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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease

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Distilling heterogeneous knowledge with aligned biological entities for histological image classification

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Learnable color space conversion and fusion for stain normalization in pathology images

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Progress in the application of AI in the standardization of traditional Chinese medicine: A review based on machine learning and deep learning

Meng, X., Lang, Y., Li, X., Li, Y., Bu, Z., Wu, Y., & Zhang, S. (2025) · Pharmacological Research - Modern Chinese Medicine · doi:10.1016/j.prmcm.2025.100639 · Cited by 12

Artificial Intelligence Meets Whole Slide Images: Deep Learning Model Shapes an Immune-Hot Tumor and Guides Precision Therapy in Bladder Cancer

Jiang, Y., Huang, S., Zhu, X., Liang Cheng, C., Liu, W., Chen, Q., & Yang, D. (2022) · Journal of Oncology · doi:10.1155/2022/8213321 · Cited by 12

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

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Digital and Computational Pathology: A Specialty Reimagined

Kiehl, T. (2022) · Future of business and finance · doi:10.1007/978-3-030-99838-7_12 · Cited by 13

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

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On Transferability of Histological Tissue Labels in Computational Pathology

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Epidemiology of hemorrhagic fever with renal syndrome in Tai’an area

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Machine learning for tissue diagnostics in oncology: brave new world

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Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial IntelligencePreprint

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Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment

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A New Intra Fine-Tuning Method Between Histopathological Datasets in Deep Learning

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Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis

Luo, X., Qu, L., Guo, Q., Song, Z., & Wang, M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3298798 · Cited by 11

Artificial Intelligence in pathology: current applications, limitations, and future directions

Sajithkumar, A., Thomas, J., Saji, A. M., Ali, F., Haneena Hasin, E. K., Adampulan, H. A. G., & Sarathchand, S. (2023) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-023-03479-3 · Cited by 13

Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics

Li, Z., Yan, C., Zhang, X., Gharibi, G., Yin, Z., Jiang, X., & Malin, B. (2023) · PubMed Central · doi:10.48550/arxiv.2308.11027 · Cited by 11

Automated curation of large‐scale cancer histopathology image datasets using deep learning

Hilgers, L., Laleh, N. G., West, N. P., Westwood, A. C., Hewitt, K. J., Quirke, P., Grabsch, H. I., Carrero, Z. I., Matthaei, E., Loeffler, C. M. L., et al. (2024) · Histopathology · doi:10.1111/his.15159 · Cited by 11

Artificial intelligence methods available for cancer research

Murmu, A., & Győrffy, B. (2024) · Frontiers of Medicine · doi:10.1007/s11684-024-1085-3 · Cited by 15

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence

Khozin, S. (2024) · Clinical and Translational Science · doi:10.1111/cts.70001 · Cited by 12

The Use of Artificial Intelligence in Predicting Chemotherapy-Induced Toxicities in Metastatic Colorectal Cancer: A Data-Driven Approach for Personalized Oncology

Froicu, E.-M., Oniciuc, O.-M., Afrăsânie, V.-A., Marinca, M. V., Riondino, S., Dumitrescu, E. A., Alexa‐Stratulat, T., Radu, I., Miron, L., Bacoanu, G., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14182074 · Cited by 14

Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study

He, M., Ye, H., Liu, L., Yao, S., Li, Z., Fan, X., Feng, L., Tong, T., Cui, Y., Yang, X., et al. (2024) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbae030 · Cited by 11

Restoring H&E stain in faded slides via phase-to-color virtual staining in near-infrared

Chae, H., Kim, J. H., Jeon, J., Lee, K., Lee, K. C., Choi, J. U., Kang, S., Choi, S., Bang, G., Lee, J.-H., et al. (2024) · APL Photonics · doi:10.1063/5.0226277 · Cited by 11

BAPLe: Backdoor Attacks on Medical Foundational Models Using Prompt Learning

Hanif, A., Shamshad, F., Awais, M., Naseer, M., Khan, F. S., Nandakumar, K., Khan, S. H., & Anwer, R. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_42 · Cited by 11

Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma

Baheti, B., Rai, S., Innani, S., Mehdiratta, G., Bell, R., Guntuku, S. C., Nasrallah, M. P., & Bakas, S. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100797 · Cited by 12

NCT-CRC-HE: Not All Histopathological Datasets are Equally Useful

Ignatov, A., & Malivenko, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91721-9_19 · Cited by 13

Detection, localization, segmentation, and classification in colorectal cancer screening using deep learning: A systematic review

Islam, M. R., Ahamed, M. F., Islam, M. R., Nahiduzzaman, M., & Ahsan, M. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108202 · Cited by 12

Unsupervised Tumor Characterization via Conditional Generative Adversarial Networks

Vu, Q. D., Kim, K., & Kwak, J. T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2993560 · Cited by 11

Neural networks and robotic microneedles enable autonomous extraction of plant metabolites

Bae, H., Paludan, M. V., Knoblauch, J., & Jensen, K. H. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab178 · Cited by 11

DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs

Bakht, A. B., Javed, S., Gilani, S. Q., Karki, H., Muneeb, M., & Werghi, N. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00797-x · Cited by 11

Multi-scope Analysis Driven Hierarchical Graph Transformer for Whole Slide Image Based Cancer Survival Prediction

Hou, W., He, Y., Yao, B., Yu, L., Yu, R., Gao, F., & Wang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_72 · Cited by 11

Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis

Zhao, K., Li, Z., Li, Y., Yao, S., Huang, Y., Wang, Y., Zhang, F., Wu, L., Chen, X., Liang, C., et al. (2020) · OncoImmunology · doi:10.1080/2162402x.2020.1841935 · Cited by 10

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Wang, L. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/7996195 · Cited by 11

Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification

Shuai, W., & Li, J. (2022) · Electronics · doi:10.3390/electronics11091510 · Cited by 11

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological imagesPreprint

Yu, G., Xie, T., Xu, C., Shi, X. M., Wu, C., Sun, K., Meng, R.-Q., Meng, X., Wang, K.-S., Xiao, H., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.13.201582 · Cited by 11

An Overview of Predictive Biomarkers and Detection Approaches for Immunotherapy Response in GI Malignancies

Chai, X., Zhang, Y., Shi, Z., Yang, R., Liu, X., Zhou, Y., Li, C., & Li, Z. (2025) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16930 · Cited by 10

Comparative analysis of high- and low-level deep learning approaches in microsatellite instability prediction

Park, J., Chung, Y. R., & Nose, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-16283-3 · Cited by 10

Enhancing Colorectal Cancer Histological Image Classification Using Transfer Learning and ResNet50 CNN Model

Peng, C., & Lee, B.-R. (2023) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.1109/ecbios57802.2023.10218590 · Cited by 10

Center-Focused Affinity Loss for Class Imbalance Histology Image Classification

Mahbub, T., Obeid, A., Javed, S., Dias, J. M. M., Hassan, T., & Werghi, N. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3336372 · Cited by 13

Multi-Scale Feature Alignment for Continual Learning of Unlabeled Domains

Thandiackal, K., Piccinelli, L., Gupta, R., Pati, P., & Göksel, O. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3368365 · Cited by 15

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

Kazemi, A., Rasouli‐Saravani, A., Gharib, M., Albuquerque, T., Eslami, S., & Schüffler, P. J. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108306 · Cited by 10

A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer

Lv, Z., Yan, R., Lin, Y., Gao, L., Zhang, F., & Wang, Y. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020012 · Cited by 10

CCSI: Continual Class-Specific Impression for data-free class incremental learning

Ayromlou, S., Tsang, T. S. M., Abolmaesumi, P., & Li, X. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103239 · Cited by 10

MobileDenseNeXt: Investigations on biomedical image classification

Tuncer, I., Doğan, Ş., & Tuncer, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124685 · Cited by 12

Continual Domain Incremental Learning for Privacy-Aware Digital Pathology

Kumari, P., Reisenbüchler, D., Luttner, L., Schaadt, N. S., Feuerhake, F., & Merhof, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_4 · Cited by 13

PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning

Hussein, N., Shamshad, F., Naseer, M., & Nandakumar, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_65 · Cited by 10

Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types

Chao, S. X., & Belanger, D. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00377 · Cited by 10

Computational Intelligence-Based Gene Expression Analysis in Colorectal Cancer: A Review

Srivastava, A., Rai, S., Singh, M. P., & Srivastava, S. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9221-5_22 · Cited by 10

S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning

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