e-pet

e-pet's Stars

• pola-rs/polars

  Dataframes powered by a multithreaded, vectorized query engine, written in Rust

  Language:Rust30.3k1679.1k2k

• KindXiaoming/pykan

  Kolmogorov Arnold Networks

  Language:Jupyter Notebook15k1134061.4k

• facebookresearch/dinov2

  PyTorch code and models for the DINOv2 self-supervised learning method.

  Language:Jupyter Notebook9.2k95402818

• mahmoodlab/CLAM

  Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

  Language:Python1.1k22256365

• dssg/aequitas

  Bias Auditing & Fair ML Toolkit

  Language:Python6924389113

• MIC-DKFZ/nnDetection

  nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

  Language:Python5531926697

• fgnt/nara_wpe

  Different implementations of "Weighted Prediction Error" for speech dereverberation

  Language:Python4901837165

• mljar/supertree

  Visualize decision trees in Python

  Language:Python4104239

• mahmoodlab/UNI

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

  Language:Jupyter Notebook33974046

• MIC-DKFZ/MedNeXt

  [MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

  Language:Python33992926

• 935963004/LaBraM

  ICLR 2024 spotlight

  Language:Python23314635

• xmindflow/Awesome-Foundation-Models-in-Medical-Imaging

  A curated list of foundation models for vision and language tasks in medical imaging

  2084014

• SPOClab-ca/BENDR

  Language:Python17791857

• tky823/ssspy

  A Python toolkit for sound source separation.

  Language:Python126610312

• thuml/SimMTM

  About Code release for "SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling" (NeurIPS 2023 Spotlight), https://arxiv.org/abs/2302.00861

  Language:Python11261014

• mahmoodlab/PANTHER

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

  Language:Jupyter Notebook9232311

• mahmoodlab/TriPath

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

  Language:Python92656

• ModelOriented/fairmodels

  Flexible tool for bias detection, visualization, and mitigation

  Language:R8672615

• mahmoodlab/TANGLE

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

  Language:Python80352

• Emory-HITI/AI-Vengers

  Language:Jupyter Notebook5971022

• UMEssen/saros-dataset

  Sparsely Annotated Region and Organ Segmentation (SAROS) - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data

  Language:Python32405

• aida-ugent/fairret

  A fairness library in PyTorch.

  Language:Python26311

• MIC-DKFZ/probabilistic_unet

  A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

  Language:Jupyter Notebook231119

• FraunhoferMEVIS/MedicalMultitaskModeling

  Training foundational medical imaging models using multi-task learning.

  Language:Python20521

• ipis-mjkim/caueeg-ceednet

  This repository is the official implementation of "Deep learning-based EEG analysis to classify mild cognitive impairment for early detection of dementia: algorithms and benchmarks" from the CNIR (CAU NeuroImaging Research) team.

  Language:Jupyter Notebook20228

• FairMedFM/FairMedFM

  Language:Jupyter Notebook19111

• moonwatcher-ai/moonwatcher

  Evaluation & testing framework for computer vision models

  Language:Python16202

• natalialmg/BlindParetoFairness

  Language:Jupyter Notebook5300

• schufa-innovationlab/fair-scoring

  Fairness metrics for continuous risk scores

  Language:Python4101

• FDZ-Gesundheit/datensatzbeschreibung_fdz_gesundheit

  Dieses Repository enthält eine vorläufige Datensatzbeschreibung, welche über die Struktur der in Zukunft über das FDZ Gesundheit zugänglichen Daten informiert.

  Language:HTML3000