🔥🔥 This is a collection of awesome articles about Foundation Models in Pathology Image Analysis🔥🔥
Foundation models have gained popularity in recent years for a broad range of pathological imaging applications.
With the aim of providing easier access for researchers, this repo contains a comprehensive paper list of Foundation models in Pathology Image Analysis, including papers, codes, and related websites.
We considered a sum of 102 research papers spanning from 2022 to 2026.
(Each section is ordered by the publication dates)
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📜 Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
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📜 Transformer-based unsupervised contrastive learning for histopathological image classification
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📜 Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
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📜 Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
- 📖 Preprint: MedRxiv, 2023
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📜 A foundation model for clinical-grade computational pathology and rare cancers detection
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📜 Rotation-Agnostic Image Representation Learning for Digital Pathology
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📜 RudolfV: A Foundation Model by Pathologists for Pathologists
- 📖 Preprint: arXiv, 2024
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📜 Towards a general-purpose foundation model for computational pathology
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📜 Computational Pathology at Health System Scale- Self-Supervised Foundation Models from Billions of Images
- 📖 AAAI 2024 Spring Symposium
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📜 A whole-slide foundation model for digital pathology from real-world data
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📜 PLUTO: Pathology-Universal Transformer
- 📖 ICML 2024 FM-Wild Workshop
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📜 A generalizable pathology foundation model using a unified knowledge distillation pretraining framework
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📜 PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains
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📜 Multistain Pretraining for Slide Representation Learning in Pathology
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📜 VIRCHOW 2: SCALING SELF-SUPERVISED MIXED MAGNIFICATION MODELS IN PATHOLOGY
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📜 Rotation-agnostic image representation learning for digital pathology
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📜 Tissue Concepts: supervised foundation models in computational pathology
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📜 A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images
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📜 Foundation models for fast, label-free detection of glioma infiltration
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📜 SegAnyPath: A Foundation Model for Multi-resolution Stain-variant and Multi-task Pathology Image Segmentation
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📜 Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
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📜 A visual-language foundation model for pathology image analysis using medical Twitter
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📜 Quilt-1M: One Million Image-Text Pairs for Histopathology
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📜 A visual-language foundation model for computational pathology
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📜 Knowledge-Enhanced Visual-Language Pretraining for Computational Pathology
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📜 PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
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📜 Transcriptomics-guided Slide Representation Learning in Computational Pathology
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📜 CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
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📜 A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
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📜 PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
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📜 Benchmarking PathCLIP for Pathology Image Analysis
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📜 A pathology foundation model for cancer diagnosis and prognosis prediction
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📜 CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
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📜 A vision–language foundation model for precision oncology
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📜 A visual–omics foundation model to bridge histopathology with spatial transcriptomics
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📜 PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology
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📜 A multimodal generative AI copilot for human pathology
- 📖 Journal: Nature, 2024
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📜 PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
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📜 Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
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📜 SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
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📜 CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology
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📜 WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image
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📜 Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
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📜 Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learning
- 📜 Generating dermatopathology reports from gigapixel whole slide images with HistoGPT
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📜 Text-Guided Foundation Model Adaptation for Pathological Image Classification
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📜 Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning
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📜 CLIPath: Fine-tune CLIP with Visual Feature Fusion for Pathology Image Analysis Towards Minimizing Data Collection Efforts
- 📖 Conference: ICCVW, 2023
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📜 Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning
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📜 The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
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📜 Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
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📜 Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
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📜 Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
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📜 PathoTune: Adapting Visual Foundation Model to Pathological Specialists
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📜 VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Human Annotation-Free Pathological Image Classification
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📜 Prompting Vision Foundation Models for Pathology Image Analysis
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📜 ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
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📜 Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
- 📖 Conference: ECCV, 2024
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📜 Prompting Whole Slide Image Based Genetic Biomarker Prediction
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📜 MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
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📜 AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
- 📖 Conference: BMVC, 2023
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📜 CellViT: Vision Transformers for precise cell segmentation and classification
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📜 All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning
- 📖 Journal of Physics: Conference Series
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📜 SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
- 📖 MICCAI 2023 Workshops
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📜 TPRO: Text-Prompting-Based Weakly Supervised Histopathology Tissue Segmentation
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📜 SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
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📜 Evaluation and Improvement of Segment Anything Model for Interactive Histopathology Image Segmentation
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📜 Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
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📜 Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
- 📖 Preprint: arXiv, 2024
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📜 WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
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📜 GlandSAM: Injecting Morphology Knowledge Into Segment Anything Model for Label-Free Gland Segmentation
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📜 Improving Mitosis Detection on Histopathology Images Using Large Vision-Language Models
- 📖 ISBI, 2024
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📜 Zero-Shot Nuclei Detection via Visual-Language Pre-trained Models
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📜 SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival
- 📖 BIBM, 2024
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📜 SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
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📜 Automatic Report Generation for Histopathology Images Using Pre-Trained Vision Transformers and BERT
- 📖 ISBI, 2024
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📜 Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology
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📜 Distilled Prompt Learning for Incomplete Multimodal Survival Prediction
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📜 ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control
- 📖 IEEE Transactions on Medical Imaging, 2025