Spatial biology processing suite

MIPHEI-ViT: Repository to train Image-to-Image H&E to Immunofluorescence models

This repository contains the package SpatialFusion, a deep-learning multimodal model for niche discovery in spatial transcritomics and histopathological data.

Code for a deep learning model capable of identifying optimal reduced CyCIF marker panels and imputing additional markers

A parameter-efficient mixture-of-experts module for computational pathology - ICLR

VISTACT: A framework for virtual histological staining of micro-computed tomography

Spatial Expression-Aligned Learning to improve pathology foundation models

Reference PyTorch implementation and models for DINOv3

[MIDL 2025] Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

Feather - Lightweight supervised slide foundation models (ICML 2025)

Standardized benchmark for computational pathology foundation models.

A PyTorch library and evaluation platform for end-to-end compression research

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

Toolkit for large-scale whole-slide image processing.

Multimodal Whole Slide Foundation Model for Pathology - Nature Medicine

Multimodal prototyping for cancer survival prediction - ICML 2024

Integrating histology and spatial transcriptomics - NeurIPS 2024

Transcriptomics-guided Slide Representation Learning in Computational Pathology - CVPR 2024

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology - CVPR 2024

Demographic bias in misdiagnosis by computational pathology models - Nature Medicine

[Nature Machine Intelligence 2024] Code and evaluation repository for the paper

Pathology Foundation Model - Nature Medicine

Analysis of 3D pathology samples using weakly supervised AI - Cell

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

Medical imaging processing for AI applications.

Variance pooling to incorporate ITH in CPath models - MICCAI 2022