HIPPO 🦛 is an explainable AI method and toolkit for weakly-supervised models in computational pathology. It enables hypothesis testing on tissue and searching for high-effect and low-effect tissue …

Code for "SemDeDup", a simple method for identifying and removing semantic duplicates from a dataset (data pairs which are semantically similar, but not exactly identical).

Evaluation framework for oncology foundation models (FMs)

Corresponding code of 'Quiros A.C.+, Coudray N.+, Yeaton A., Yang X., Chiriboga L., Karimkhan A., Narula N., Pass H., Moreira A.L., Le Quesne J.*, Tsirigos A.*, and Yuan K.* Mapping the landscape o…

PyTorch code for hierarchical k-means -- a data curation method for self-supervised learning

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

[T-PAMI-2024] Transformer-Based Visual Segmentation: A Survey

Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)

Towards a general-purpose foundation model for computational pathology - Nature Medicine

[Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)

A collection of resources on applications of Transformers in Medical Imaging.

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

Encoder-Decoder Cell and Nuclei segmentation models

The code implementation for cell segmentation

Benchmarking toolkit for patch-based histopathology image classification.

Code for the im4MEC model described in the paper 'Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide ima…

A pipeline to segment tissue from the background in histological images

Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

cGAN-based Multi Organ Nuclei Segmentation

Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to ex…

Code associated to the publication: Scaling self-supervised learning for histopathology with masked image modeling, A. Filiot et al., MedRxiv (2023). We publicly release Phikon 🚀

Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

Ressources of histopathology datasets

Open source tools for computational pathology - Nature BME

Interactive Segmentation of Nucleus