Stars
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Medical imaging processing for AI applications.
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.
List of how-to tutorials, mostly for Debian/Ubuntu/Linux related stuff.
A pytorch library for graph and hypergraph computation.
A thesis template compliant with King's College London and UCL rules
Fast and modular sklearn replacement for generalized linear models
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Python implementation of bulk RNAseq deconvolution algorithms
A high-level federated learning Python library used to run complex federated learning experiments at scale on a Substra network
Library to compute surface distance based performance metrics for segmentation tasks.
"pip install unet": PyTorch Implementation of 1D, 2D and 3D U-Net architecture.
Low-level Python library used to interact with a Substra network
GrAIdient is a deep learning framework that aims at challenging the way we train and run models.
List of surgical tool datasets organised by task.
A playbook for systematically maximizing the performance of deep learning models.
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 🚀
Simple video summarisation Python package.
Implementation of the paper: 'Robust mixture modelling using the t distribution', D. Peel and G. J. McLachlan.
A collection of loss functions for medical image segmentation