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Awesome List of Digital and Computational Pathology Resources
https://github.com/open-pathology/awesome-pathology

List: awesome-pathology

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Awesome List of Digital and Computational Pathology Resources

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README 

        
# Awesome Digital and Computational Pathology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



This curated list of useful resources is supported by:



  

  

  Paige AI



## Contents



- [Software](#software)

  - [Assistant](#assistant) 

  - [Image Analysis](#image-analysis)

  - [Image IO](#image-io)

  - [Machine Learning](#machine-learning)

  - [Model](#model)

  - [Foundation Model](#foundation-model)

  - [Platform](#platform)

  - [Viewer](#viewer)

  - [Viewer (Free)](#viewer-free)

- [Data](#data)

  - [Challenges](#challenges)

  - [Datasets](#datasets)

  - [References](#references)

- [Publications](#publications)

  - [Papers](#papers)



## Software



### Assistant



- [Digital Pathology Assistant](https://chatgpt.com/g/g-L1IbnIIVt-digital-pathology-assistant-v3-0) - Specify your requirements in plain english and I'll provide PathML and Python code for your use-case.



### Image Analysis



- [HistomicsTK](https://github.com/DigitalSlideArchive/HistomicsTK/) - Toolkit for the analysis of digital pathology images.

- [HistoQC](https://github.com/choosehappy/HistoQC/) - Quality control tools for digital pathology.

- [PathProfiler](https://github.com/MaryamHaghighat/PathProfiler/) - Quality assessment of histopathology WSI cohorts.

- [PyHIST](https://github.com/manuel-munoz-aguirre/PyHIST/) - Histological image segmentation tool.

- [pyslide](https://github.com/PingjunChen/pyslide/) - Digital pathology WSI analysis toolbox.

- [TIA Toolbox](https://github.com/TissueImageAnalytics/tiatoolbox/) - Computational pathology toolbox that provides an end-to-end API for pathology image analysis.



### Image IO



- [Bio-Formats](https://github.com/ome/bioformats/) - Java software tool for reading and writing microscopy image using standardized, open formats.

- [compay-syntax](https://github.com/jgamper/compay-syntax/) - Tissue mask and tiling pipeline.

- [cuCIM](https://github.com/rapidsai/cucim/) - NVIDIA's accelerated computer vision and image processing software library for multidimensional images.

- [libvips](https://www.libvips.org/) - A fast image processing library with low memory needs.

- [OpenSlide](https://openslide.org/) - Provides a simple C interface with Python bindings to read WSIs in multiple formats.

- [svg2svs](https://github.com/Ellogon/svg2svs/) - Generate checkerboard and build multi-layer pyramidal SVS files from SVG images.

- [tifffile](https://github.com/cgohlke/tifffile/) - Read and write TIFF-like files using in bioimaging.

- [WholeSlideData](https://github.com/DIAGNijmegen/pathology-whole-slide-data/) - Batch iterator that enables fast, efficient and easy patch sampling.



### Machine Learning



- [DLUP](https://github.com/nki-ai/dlup/) - Deep learning utilities for pathology.

- [eva](https://github.com/kaiko-ai/eva/) - Evaluation framework for oncology foundation models.

- [histocartography](https://github.com/BiomedSciAI/histocartography/) - Library designed to facilitate the development of graph-based computational pathology pipelines.

- [nuclei.io](https://github.com/huangzhii/nuclei.io/) - Human-in-the-loop active learning framework for pathology image analysis.

- [PathML](https://github.com/Dana-Farber-AIOS/pathml/) - Tools for computational pathology.

- [Slideflow](https://slideflow.dev/) - Python package that provides a unified API for building and testing deep learning models for histopathology.



### Model



- [ACMIL](https://github.com/dazhangyu123/ACMIL/) - WSI classification.

- [BEPH](https://github.com/Zhcyoung/BEPH/) - BEiT-based model pre-training on WSIs.

- [Cell-DETR](https://github.com/ChristophReich1996/Cell-DETR/) - Attention-based transformers for instance segmentation of cells in microstructures.

- [CellViT](https://github.com/TIO-IKIM/CellViT/) - Vision transformers for precise cell segmentation and classification.

- [Cerberus](https://github.com/TissueImageAnalytics/cerberus/) - Multi-task learning enables simultaneous histology image segmentation and classification.

- [CLAM](https://github.com/mahmoodlab/CLAM/) - Data-efficient and weakly supervised computational pathology on WSI.

- [DeepLIIF](https://github.com/nadeemlab/DeepLIIF/) - Deep-learning inferred multiplex immunofluorescence for immunohistochemical image quantification.

- [DiffInfinite](https://github.com/marcoaversa/diffinfinite/) - Large mask-image synthesis via parallel random patch diffusion in histopathology.

- [DMMN](https://github.com/MSKCC-Computational-Pathology/DMMN/) - Deep Multi-Magnification Network for multi-class tissue segmentation of WSI.

- [DT-MIL](https://github.com/yfzon/DT-MIL/) - Deformable transformer for multi-instance learning on histopathological image.

- [FrOoDo](https://github.com/MECLabTUDA/FrOoDo/) - Framework for out of distribution detection.

- [fseg](https://github.com/deepathology/fseg/) - Unsupervised semantic segmentation for pathology by factorizing foundation model features.

- [HistoGPT](https://github.com/marrlab/HistoGPT/) - Generating highly accurate histopathology reports from whole slide images.

- [HistoSegNet](https://github.com/lyndonchan/hsn_v1/) - Semantic segmentation of histological tissue type in WSIs.

- [HoVer-Net](https://github.com/vqdang/hover_net/) - Simultaneous segmentation and classification of nuclei in multi-tissue histology images.

- [LongViT](https://github.com/microsoft/torchscale/blob/main/examples/longvit/) - Vision Transformer that can process gigapixel images in an end-to-end manner.

- [MCAT](https://github.com/mahmoodlab/MCAT/) - Multimodal co-attention transformer for survival prediction in gigapixel WSIs.

- [MMP](https://github.com/mahmoodlab/MMP/) - Multimodal prototyping for cancer survival prediction.

- [MSINet](https://github.com/rikiyay/MSINet/) - Deep learning model for the prediction of microsatellite instability in colorectal cancer.

- [PANTHER](https://github.com/mahmoodlab/Panther/) - Morphological prototyping for unsupervised slide representation learning in computational pathology.

- [Patch-GCN](https://github.com/mahmoodlab/Patch-GCN/) - WSI are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks.

- [RSP](https://github.com/srinidhiPY/SSL_CR_Histo/) - Self-supervised driven consistency training for annotation efficient histopathology image analysis.

- [Snuffy](https://github.com/jafarinia/snuffy/) - Efficient WSI classifier.

- [SparseConvMIL](https://github.com/MarvinLer/SparseConvMIL/) - Sparse convolutional context-aware multiple instance learning for WSI classification.

- [StainGAN](https://github.com/xtarx/StainGAN/) - Stain style transfer for digital histological images.

- [stainlib](https://github.com/sebastianffx/stainlib/) - Augmentation & normalization of H&E images.

- [StainTools](https://github.com/Peter554/StainTools/) - Tools for tissue image stain normalisation and augmentation.

- [StarDist](https://github.com/stardist/stardist/) - Object detection with star-convex shapes.

- [TANGLE](https://github.com/mahmoodlab/TANGLE/) - Transcriptomics-guided slide representation learning in computational pathology.

- [TCGA segmentation](https://github.com/MarvinLer/tcga_segmentation/) - Weakly supervised multiple instance learning histopathological tumor segmentation.

- [torchstain](https://github.com/EIDOSLAB/torchstain/) - Stain normalization transformations.

- [TransMIL](https://github.com/szc19990412/TransMIL/) - Transformer based correlated multiple instance learning for WSI classification.



### Foundation Model



- [CONCH](https://github.com/mahmoodlab/CONCH/) - Vision-language foundation model for computational pathology.

- [Hibou](https://github.com/HistAI/hibou/) - A family of foundational vision transformers for pathology.

- [HIPT](https://github.com/mahmoodlab/HIPT/) - Scaling vision transformers to gigapixel images via hierarchical self-supervised learning.

- [H-optimus](https://github.com/bioptimus/releases/tree/main/models/h-optimus/v0/) - Foundation model for histology.

- [PathDino](https://kimialabmayo.github.io/PathDino-Page/) - Rotation-agnostic image representation learning for digital pathology.

- [Path Foundation](https://huggingface.co/google/path-foundation/) - Embedding model for efficiently building AI for histopathology applications.

- [PathoDuet](https://github.com/openmedlab/PathoDuet/) - Foundation models for pathological slide analysis of H&E and IHC stains.

- [Phikon](https://github.com/owkin/HistoSSLscaling/) - Scaling self-supervised learning for histopathology with masked image modeling.

- [Prov-GigaPath](https://github.com/prov-gigapath/prov-gigapath/) - A whole-slide foundation model for digital pathology from real-world data.

- [ROAM](https://github.com/whiteyunjie/ROAM/) - A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular state revelation of gliomas.

- [TransPath](https://github.com/Xiyue-Wang/TransPath/) - Transformer-based unsupervised contrastive learning for histopathological image classification.

- [UNI](https://github.com/mahmoodlab/UNI/) - General-purpose foundation model for computational pathology.

- [VIM4Path](https://github.com/AtlasAnalyticsLab/Vim4Path/) - Self-supervised vision mamba for WSIs.

- [Virchow](https://huggingface.co/paige-ai/Virchow/) - Self-supervised vision transformer pretrained using 1.5M WSIs.



### Platform



- [Digital Slide Archive](https://digitalslidearchive.github.io/digital_slide_archive/) - Provides the ability to store, manage, visualize and annotate large imaging datasets.



### Viewer



- [ASAP](https://computationalpathologygroup.github.io/ASAP/) - Desktop application for visualizing, annotating and automatically analyzing WSIs.

- [Cytomine](https://doc.cytomine.org/) - Collaborative analysis of WSIs.

- [DigiPathAI](https://github.com/haranrk/DigiPathAI/) - Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay.

- [HistomicsUI](https://github.com/DigitalSlideArchive/HistomicsUI/) - Web interface to visualize WSI and manage annotations.

- [slim](https://github.com/ImagingDataCommons/slim/) - Interoperable web-based slide microscopy viewer and annotation tool.

- [QuickAnnotator](https://github.com/choosehappy/QuickAnnotator/) - Model assisted tool for rapid annotation of WSIs.

- [QuPath](https://qupath.github.io/) - Java application that enables researchers and pathologists to visualize, analyze and annotate WSIs.



### Viewer (Free)



- [Aperio ImageScope](https://www.leicabiosystems.com/en-ca/digital-pathology/manage/aperio-imagescope/) - Freely downloadable software for viewing WSIs. Windows only.

- [PathPresenter](https://www.pathpresenter.com/) - A complete enterprise workflow platform built by pathologists.



## Data



### Challenges



- [ACDC](https://acdc-lunghp.grand-challenge.org/) - Automatic Cancer Detection and Classification of lung histopathology.

- [ACROBAT](https://acrobat.grand-challenge.org/) - AutomatiC Registration Of Breast cAncer Tissue.

- [ANHIR](https://anhir.grand-challenge.org/) - Automatic Non-rigid Histological Image Registration.

- [BACH](https://iciar2018-challenge.grand-challenge.org/) - BreAst Cancer Histology images.

- [BCI](https://bci.grand-challenge.org/) - Breast Cancer Immunohistochemical image generation.

- [BreastPathQ](https://breastpathq.grand-challenge.org/) - Quantitative biomarkers for the determination of cancer cellularity.

- [CAMELYON16](https://camelyon16.grand-challenge.org/) - Cancer metastasis detection in lymph node.

- [CAMELYON17](https://camelyon17.grand-challenge.org/) - Building on CAMELYON16 by moving from slide level analysis to patient level analysis.

- [CellSeg](https://neurips22-cellseg.grand-challenge.org/) - Cell segmentation in multi-modality high-resolution microscopy images.

- [CoNIC](https://conic-challenge.grand-challenge.org/) - Colon Nuclei Identification and Counting.

- [DigestPath 2019](https://digestpath2019.grand-challenge.org/) - Digestive-system pathological detection and segmentation.

- [ENDO-AID](https://endo-aid.grand-challenge.org/) - Endometrial carcinoma detection in pipelle biopsies.

- [Gleason 2019](https://gleason2019.grand-challenge.org/) - Automatic Gleason grading of prostate cancer in digital histopathology.

- [HER2 Scoring Contest](https://warwick.ac.uk/fac/cross_fac/tia/data/her2contest/) - Automated HER2 scoring algorithms in WSI of breast cancer tissues.

- [HEROHE](https://ecdp2020.grand-challenge.org/) - Predicting HER2 status in breast cancer from H&E.

- [KPIs](https://sites.google.com/view/kpis2024/) - Kidney Pathology Image segmentation.

- [LEOPARD](https://leopard.grand-challenge.org/leopard/) - LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes.

- [LYSTO](https://lysto.grand-challenge.org/) - LYmphocytes aSsessment hackathOn in immunohistochemically stained tissue sections.

- [LYON19](https://lyon19.grand-challenge.org/) - LYmphocyte detectiON in IHC stained specimens.

- [MIDOG 2021](https://imig.science/midog2021/) - MItosis DOmain Generalization on tissue preparation and image acquisition.

- [MIDOG 2022](https://imig.science/midog/) - MItosis DOmain Generalization on tissue types.

- [MITOS-ATYPIA-14](https://mitos-atypia-14.grand-challenge.org/) - Detection of mitosis and evaluation of nuclear atypia score.

- [MONKEY](https://monkey.grand-challenge.org/) - Machine-learning for Optimal detection of iNflammatory cells in the KidnEY.

- [MoNuSAC](https://monusac-2020.grand-challenge.org/) - Multi-Organ NUclei Segmentation And Classification.

- [MoNuSeg](https://monuseg.grand-challenge.org/) - Multi-Organ NUclei Segmentation.

- [PAIP2019](https://paip2019.grand-challenge.org/) - Liver cancer segmentation.

- [PAIP2020](https://paip2020.grand-challenge.org/) - Classification and segmentation of microsatellite instability (MSI) in colorectal cancer.

- [PAIP2021](https://paip2021.grand-challenge.org/) - Perineural invasion in multiple organ cancer.

- [PAIP2023](https://2023paip.grand-challenge.org/) - Tumor cellularity prediction in pancreatic cancer and colon cancer.

- [PANDA](https://www.kaggle.com/competitions/prostate-cancer-grade-assessment/) - Prostate cANcer graDe Assessment.

- [SegPC](https://segpc-2021.grand-challenge.org/) - Segmentation of multiple myeloma in Plasma Cells.

- [TIGER](https://tiger.grand-challenge.org/) - Fully automated assessment of tumor-infiltrating lymphocytes (TILs) in H&E breast cancer slides.

- [TUPAC16](https://tupac.grand-challenge.org/) - TUmor Proliferation Assessment.

- [WSSS4LUAD](https://wsss4luad.grand-challenge.org/) - Weakly-supervised tissue semantic segmentation for lung adenocarcinoma.



### Datasets



- [ARCH](https://warwick.ac.uk/fac/cross_fac/tia/data/arch/) - Multiple instance captioning.

- [BCNB](https://bcnb.grand-challenge.org/) - Early Breast Cancer Core-Needle Biopsy WSI dataset.

- [BCSS](https://bcsegmentation.grand-challenge.org/) - Breast Cancer Semantic Segmentation.

- [BRACS](https://www.bracs.icar.cnr.it/) - BReAst Carcinoma Subtyping.

- [CRC](https://zenodo.org/record/1214456/) - 100,000 histological images of human colorectal cancer and healthy tissue.

- [CryoNuSeg](https://github.com/masih4/CryoNuSeg/) - Nuclei segmentation of cryosectioned H&E-stained histological images.

- [H2T](https://github.com/vqdang/H2T/) - Handcrafted Histological Transformer for unsupervised representation of WSIs.

- [HEST](https://github.com/mahmoodlab/hest/) - Bringing spatial transcriptomics and histopathology together.

- [LC25000](https://github.com/tampapath/lung_colon_image_set/) - Lung and colon cancer histopathological image dataset.

- [LyNSeC](https://zenodo.org/records/8065174/) - Lymphoma nuclear segmentation and classification dataset.

- [MHIST](https://bmirds.github.io/MHIST/) - Minimalist histopathology image analysis dataset.

- [NuInsSeg](https://www.kaggle.com/datasets/ipateam/nuinsseg/) - A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

- [NuCLS](https://sites.google.com/view/nucls/home/) - A scalable crowdsourcing approach & dataset for nucleus classification, localization and segmentation in breast cancer.

- [OCELOT](https://lunit-io.github.io/research/publications/ocelot/) - Overlapped cell on tissue dataset for histopathology.

- [PanNuke](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke/) - Dataset for nuclei instance segmentation and classification.

- [PCAM](https://github.com/basveeling/pcam/) - PatchCamelyon provides a new benchmark for machine learning models akin to CIFAR-10 and MNIST.

- [TCGA](https://www.cancer.gov/ccg/research/genome-sequencing/tcga/) - The Cancer Genome Atlas is a landmark cancer genomics program that molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types.

- [UNITOPATHO](https://github.com/EIDOSLAB/UNITOPATHO/) - A labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading.

- [UNMaSk](https://github.com/pathdata/UNMaSk/) - Unmasking the immune microecology of ductal carcinoma in situ.



### References



- [ADP](https://github.com/mahdihosseini/ADP/) - Atlas of digital pathology for deep learning.

- [Cytomine Collection](https://cytomine.com/collection/) - Open access to high quality WSI in several formats.

- [DICOM WSI Standard](https://dicom.nema.org/dicom/dicomwsi/) - DICOM WSI document.

- [Jerad Gardner, MD](https://www.youtube.com/@JMGardnerMD/) - Popular YouTube channel for educational videos by a pathologist.

- [WebPathology](https://www.webpathology.com/) - Visual survey of surgical pathology.



## Publications



### Papers



- [chen2022self](https://github.com/Richarizardd/Self-Supervised-ViT-Path/) - Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology.

- [kang2022benchmarking](https://lunit-io.github.io/research/publications/pathology_ssl/) - Benchmarking Self-Supervised Learning on Diverse Pathology Datasets.

- [wolflein2023good](https://github.com/georg-wolflein/good-features/) - A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification.

- [vaidya2024demographic](https://github.com/mahmoodlab/CPATH_demographics/) - Demographic bias in misdiagnosis by computational pathology models.