TransPath: Transformer-Based Self-supervised Learning for Histopathological Image Classification

ImageNet Large Scale Visual Recognition Challenge

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Momentum Contrast for Unsupervised Visual Representation Learning

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A simple framework for contrastive learning of visual representations

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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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Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

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Bottleneck Transformers for Visual Recognition

Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385 · see citing works

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

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Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations

Koohbanani, N. A., Unnikrishnan, B., Khurram, S. A., Krishnaswamy, P., & Rajpoot, N. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3056023 · Cited by 180 · see citing works

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Srinidhi, C. L., Kim, S. W., Chen, F.-D., & Martel, A. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102256 · Cited by 116 · see citing works

Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive CodingPreprint

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Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology

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The efficacy of dihydroartemisinin-piperaquine and artemether-lumefantrine with and without primaquine on Plasmodium vivax recurrence: A systematic review and individual patient data meta-analysis

Commons, R. J., Simpson, J. A., Thriemer, K., Abreha, T., Adam, I., Anstey, N. M., Assefa, A., Awab, G. R., Baird, J. K., Barber, B. E., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002928 · Cited by 54 · see citing works

Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding (Conference Presentation)

Lu, M. Y., Chen, R. J., & Mahmood, F. (2020) · SPIE Medical Imaging: Digital Pathology · doi:10.1117/12.2549627 · Cited by 49 · see citing works

Self-Supervision Closes the Gap Between Weak and Strong Supervision in HistologyPreprint

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Transformer-based unsupervised contrastive learning for histopathological image classification

Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102559 · Cited by 751 · see citing works

Transformers in medical image analysis

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Self-supervised learning for medical image classification: a systematic review and implementation guidelines

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Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187 · see citing works

In-context learning enables multimodal large language models to classify cancer pathology images

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Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

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Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., & Schiratti, J.-B. (2023) · doi:10.1101/2023.07.21.23292757 · Cited by 148 · see citing works

Vision Transformers for Computational Histopathology

Xu, H., Xu, Q., Cong, F., Kang, J., Han, C., Liu, Z., Madabhushi, A., & Lu, C. Y. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3297604 · Cited by 146 · see citing works

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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Recent progress in transformer-based medical image analysis

Liu, Z., Lv, Q., Yang, Z., Li, Y., Lee, C. H., & Shen, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107268 · Cited by 127 · see citing works

MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology

Yang, S., Wang, Y., & Chen, H. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72083-3_28 · Cited by 114 · see citing works

A survey of Transformer applications for histopathological image analysis: New developments and future directions

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From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology

Springenberg, M., Frommholz, A., Wenzel, M. A., Weicken, E., Ma, J., & Strodthoff, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102809 · Cited by 99 · see citing works

A Review of deep learning methods for denoising of medical low-dose CT images

Zhang, J., Gong, W., Ye, L., Wang, F., Shangguan, Z., & Cheng, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108112 · Cited by 96 · see citing works

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Yang, Z., Wei, T., Liang, Y., Yuan, X., Gao, R., Xia, Y., Zhou, J., Zhang, Y., & Yu, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-57587-y · Cited by 97 · see citing works

Artificial intelligence applications in histopathology

Bahadir, C. D., Omar, M., Rosenthal, J., Marchionni, L., Liechty, B., Pisapia, D. J., & Sabuncu, M. R. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-023-00012-7 · Cited by 84 · see citing works

Physics-Informed Computer Vision: A Review and Perspectives

Banerjee, C., Nguyen, K., Fookes, C., & Karniadakis, G. E. (2024) · ACM Computing Surveys · doi:10.1145/3689037 · Cited by 97 · see citing works

Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis

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Machine learning in computational histopathology: Challenges and opportunities

Cooper, M. C., Ji, Z., & Krishnan, R. G. (2023) · Genes Chromosomes and Cancer · doi:10.1002/gcc.23177 · Cited by 81 · see citing works

A survey on artificial intelligence in histopathology image analysis

Abdelsamea, M. M., Zidan, U., Senousy, Z., Gaber, M. M., Rakha, E. A., & Ilyas, M. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1474 · Cited by 80 · see citing works

W-Net: A boundary-enhanced segmentation network for stroke lesions

Wu, Z., Zhang, X., Li, F., Wang, S., Huang, L., & Li, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120637 · Cited by 75 · see citing works

Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification

Guan, Y., Zhang, J., Tian, K., Yang, S., Dong, P., Xiang, J., Yang, W., Huang, J., Zhang, Y., & Han, X. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01825 · Cited by 74 · see citing works

Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme

Pramanik, R., Banerjee, B., Efimenko, G. A., Kaplun, D., & Sarkar, R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281815 · Cited by 73 · see citing works

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images

Xiang, J., Wang, X., Wang, X., Zhang, J., Yang, S., Yang, W., Han, X., & Liu, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106340 · Cited by 70 · see citing works

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia, K., & Wang, J. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.38 · Cited by 75 · see citing works

A Recent Survey of Vision Transformers for Medical Image Segmentation

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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works

Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma

Nasrallah, M. P., Zhao, J., Tsai, C. C., Meredith, D., Marostica, E., Ligon, K. L., Golden, J. A., & Yu, K.-H. (2023) · Med · doi:10.1016/j.medj.2023.06.002 · Cited by 67 · see citing works

Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification

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ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

Stegmüller, T., Bozorgtabar, B., Spahr, A., & Thiran, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00611 · Cited by 54 · see citing works

Graph neural networks in histopathology: Emerging trends and future directions

Brussee, S., Buzzanca, G., Schrader, A. M. R., & Kers, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103444 · Cited by 59 · see citing works

StrokeViT with AutoML for brain stroke classification

Raj, R., Mathew, J., Kannath, S. K., & Rajan, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105772 · Cited by 53 · see citing works

CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading

Zhao, S., Wu, Y., Tong, M., Yao, Y., Qian, W., & Qi, S. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac9fa0 · Cited by 51 · see citing works

Transcriptomics-Guided Slide Representation Learning in Computational Pathology

Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51 · see citing works

Learned Representation-Guided Diffusion Models for Large-Image Generation

Graikos, A., Yellapragada, S., Le, M.-Q., Kapse, S., Prasanna, P., Saltz, J. H., & Samaras, D. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00815 · Cited by 50 · see citing works

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images

Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102539 · Cited by 50 · see citing works

Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma

Pan, L., Wang, H., Lian, W., Ji, B., Liu, M., Chongcheawchamnan, M., Yuan, J., & Peng, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103824 · Cited by 43 · see citing works

Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning

Ye, Y., Xie, Y., Zhang, J., Chen, Z., Wu, Q., & Xia, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01057 · Cited by 40 · see citing works

Global contrast-masked autoencoders are powerful pathological representation learners

Quan, H., Li, X., Chen, W., Bai, Q., Zou, M., Yang, R., Zheng, T., Qi, R., Gao, X., & Cui, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110745 · Cited by 39 · see citing works

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 38 · see citing works

MLP-based classification of COVID-19 and skin diseases

Zhang, R., Wang, L., Cheng, S., & Song, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120389 · Cited by 36 · see citing works

Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning

Holste, G., Oikonomou, E. K., Mortazavi, B. J., Wang, Z., & Khera, R. (2024) · Communications Medicine · doi:10.1038/s43856-024-00538-3 · Cited by 35 · see citing works

Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels

Tavolara, T., Gurcan, M. N., & Niazi, M. (2022) · Cancers · doi:10.3390/cancers14235778 · Cited by 34 · see citing works

Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer

Wang, K., Zheng, F., Cheng, L., Dai, H., Dou, Q., & Qin, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3381239 · Cited by 28 · see citing works

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 · see citing works

Multistain Pretraining for Slide Representation Learning in Pathology

Jaume, G., Vaidya, A., Zhang, A., H. Song, A., J. Chen, R., Sahai, S., Mo, D., Madrigal, E., Phi Le, L., & Mahmood, F. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73414-4_2 · Cited by 27 · see citing works

CLIPath: Fine-tune CLIP with Visual Feature Fusion for Pathology Image Analysis Towards Minimizing Data Collection Efforts

Lai, Z., Li, Z., Oliveira, L. C., Chauhan, J., Dugger, B. N., & Chuah, C. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00251 · Cited by 26 · see citing works

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT ReconstructionPreprint

Wang, C., Shang, K., Zhang, H.-M., Li, Q., Hui, Y., & Zhou, S. (2021) · arXiv.org · doi:10.48550/arxiv.2111.10790 · Cited by 39 · see citing works

From patches to WSIs: A systematic review of deep Multiple Instance Learning in computational pathology

Zhang, Y., Gao, Z., He, K., Li, C., & Mao, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103027 · Cited by 23 · see citing works

HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images

Xu, J., Huang, T., Wen, K., Chi, M., & An, H. (2022) · Mathematics · doi:10.3390/math11010110 · Cited by 23 · see citing works

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Stacke, K., Unger, J., Lundström, C. F., & Eilertsen, G. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-f9a1 · Cited by 24 · see citing works

Lesion identification in fundus images via convolutional neural network-vision transformer

Lian, J., & Liu, T. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105607 · Cited by 23 · see citing works

Artificial intelligence in liver imaging: methods and applications

Zhang, P., Gao, C., Huang, Y., Chen, X., Pan, Z., Wang, L., Dong, D., Li, S., & Qi, X. (2024) · Hepatology International · doi:10.1007/s12072-023-10630-w · Cited by 23 · see citing works

TranSOP: Transformer-Based Multimodal Classification for Stroke Treatment Outcome Prediction

Samak, Z. A., Clatworthy, P. L., & Mirmehdi, M. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230576 · Cited by 22 · see citing works

MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis

Gadermayr, M., Koller, L., Tschuchnig, M., Stangassinger, L. M., Kreutzer, C., Couillard‐Després, S., Oostingh, G. J., & Hittmair, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_46 · Cited by 21 · see citing works

Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

Das, S., Jain, T., Reilly, D., Balaji, P., Karmakar, S., Marjit, S., Li, X., Das, A., & Ryoo, M. S. (2023) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00673 · Cited by 31 · see citing works

Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo-Shapes for Transformer-Based Medical Tasks

Viriyasaranon, T., Woo, S., & Choi, J.-H. (2023) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2023.3237596 · Cited by 15 · see citing works

Masked Spectral Bands Modeling With Shifted Windows: An Excellent Self-Supervised Learner for Classification of Medical Hyperspectral Images

Li, Y., Wu, R., Tan, Q., Yang, Z., & Huang, H. B. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3273506 · Cited by 20 · see citing works

Pyramid-based self-supervised learning for histopathological image classification

Wang, J., Quan, H., Wang, C., & Yang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107336 · Cited by 20 · see citing works

Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning

Zhang, J., Kapse, S., Ma, K., Prasanna, P., Saltz, J. H., Vakalopoulou, M., & Samaras, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_60 · Cited by 19 · see citing works

Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach

Saber, A., Fateh, A., Parhami, P., Siahkarzadeh, A., Fateh, M., & Ferdowsi, S. (2025) · Sensors · doi:10.3390/s25237233 · Cited by 19 · see citing works

Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets

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Rotation-Agnostic Image Representation Learning for Digital Pathology

Alfasly, S., Shafique, A., Nejat, P., Khan, J., Alsaafin, A., Alabtah, G., & Tizhoosh, H. R. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01110 · Cited by 16 · see citing works

Robust Multimodal Survival Prediction with Conditional Latent Differentiation Variational AutoEncoder

Zhou, J., Tang, J., Zuo, Y., Wan, P., Zhang, D., & Shao, W. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00971 · Cited by 15 · see citing works

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

Li, J., Zheng, Y., Wu, K., Shi, J., Xie, F., & Jiang, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_27 · Cited by 15 · see citing works

Unsupervised pre-training of graph transformers on patient population graphs

Pellegrini, C., Navab, N., & Kazi, A. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102895 · Cited by 15 · see citing works

Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging

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Sparse and Hierarchical Transformer for Survival Analysis on Whole Slide Images

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Transformers and their application to medical image processing: A review

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MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework

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

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Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition

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Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning

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Self-distillation Augmented Masked Autoencoders for Histopathological Image Understanding

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Fourier ViT: A Multi-scale Vision Transformer with Fourier Transform for Histopathological Image Classification

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DCA-DAFFNet: An End-to-End Network With Deformable Fusion Attention and Deep Adaptive Feature Fusion for Laryngeal Tumor Grading From Histopathology Images

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Prognosis prediction based on liver histopathological image via graph deep learning and transformer

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GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

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Self-Distilled Supervised Contrastive Learning for diagnosis of breast cancers with histopathological images

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Self-Supervised Triplet Contrastive Learning for Classifying Endometrial Histopathological Images

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RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning

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Transformers in pathological image analysis: A survey

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Recent ViT based models for Breast Cancer Histopathology Image Classification

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PathTR: Context-Aware Memory Transformer for Tumor Localization in Gigapixel Pathology Images

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Generalized deep learning for histopathology image classification using supervised contrastive learning

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ZoomLDM: Latent Diffusion Model for multi-scale image generation

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Breaking Barriers in Cancer Diagnosis: Super‐Light Compact Convolution Transformer for Colon and Lung Cancer Detection

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Histopathology Image Classification Using Supervised Contrastive Deep Learning

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MedTrans: Intelligent Computing for Medical Diagnosis Using Multiscale Cross-Attention Vision Transformer

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CA2CL: Cluster-Aware Adversarial Contrastive Learning for Pathological Image Analysis

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Self-supervised Pre-training for Nuclei Segmentation

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Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images

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Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges

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Order-ViT: Order Learning Vision Transformer for Cancer Classification in Pathology Images

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Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers

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Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding

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UniCell: Universal Cell Nucleus Classification via Prompt Learning

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SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction

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Transformer-based unsupervised contrastive learning for histopathological image classification

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Pyramid-based self-supervised learning for histopathological image classification

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Efficient Multi-Stage Self-Supervised Learning for Pathology Image Analysis via Masking

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Self-Supervised Learning for Histopathology Image Analysis with Limited Labels

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Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

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