abebe9849

abebe9849's Stars

• lucidrains/vit-pytorch

  Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

  Language:Python21.4k1562693.1k

• mahmoodlab/CLAM

  Open source tools for computational pathology - Nature BME

  Language:Python1.2k21266373

• prov-gigapath/prov-gigapath

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

  Language:Python447611362

• moment-timeseries-foundation-model/moment

  MOMENT: A Family of Open Time-series Foundation Models

  Language:TypeScript41535462

• ShirAmir/dino-vit-features

  Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".

  Language:Python40642148

• qhfan/RMT

  (CVPR2024)RMT: Retentive Networks Meet Vision Transformer

  Language:Python310143824

• uni-medical/STU-Net

  The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.

  Language:Python29653427

• ibrahimethemhamamci/CT-CLIP

  Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography

  Language:Python22122922

• LeapLabTHU/EfficientTrain

  1.5−3.0× lossless training or pre-training speedup. An off-the-shelf, easy-to-implement algorithm for the efficient training of foundation visual backbones.

  Language:Python2146119

• janfreyberg/superintendent

  Practical active learning in python

  Language:Python189123018

• MedAIerHHL/CVPR-MIA

  Papers of Medical Image Analysis on CVPR

  159216

• zhuyitan/IGTD

  Image Generator for Tabular Data (IGTD): Converting Tabular Data to Images for Deep Learning Using Convolutional Neural Networks

  Language:Python15841258

• mahmoodlab/TriPath

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

  Language:Python94668

• Cassie07/PathOmics

  [MICCAI 2023 Oral] The official code of "Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction" (top 9%)

  Language:Python8321211

• bowang-lab/BLEEP

  Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

  Language:Python7051511

• ZFTurbo/timm_3d

  PyTorch Volume Models for 3D data

  Language:Python50324

• ZFTurbo/segmentation_models_pytorch_3d

  Segmentation models for 3D data with different backbones. PyTorch.

  Language:Python43467

• ml-jku/MIM-Refiner

  A Contrastive Learning Boost from Intermediate Pre-Trained Representations

  Language:Python398124

• Hendrik-code/spineps

  This is a segmentation pipeline to automatically, and robustly, segment the whole spine in T2w sagittal images.

  Language:Python341182

• nicoboou/chadavit

  Official PyTorch implementation of ChAda-ViT [CVPR 2024]

  Language:Python30441

• imatge-upc/SurvLIMEpy

  Local interpretability for survival models

  Language:Python25724

• bmi-imaginelab/SI-MIL

  SI-MIL

  Language:Python242

• Zehui127/1d-swin

  The implementation of 1d-swin, an efficient transformer for capturing hierarchical 1-dimentional long range sequence

  Language:Python17435

• wds-seu/DeepGene

  Language:Python16201

• selimsef/kaggle-identify-contrails-4th

  Language:Python12201

• jinseikenai/glomeruli_segmentation

  Pathohistlogical glomerular segementation on whole slide images using Faster R-CNN and ESPNet.

  Language:Python9141

• bdsp-core/IIIC-IRR

  Code to reproduce figures in "Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs"

  Language:MATLAB4112

• ShunsukeKikuchi/MedAI6

  4th Place Solution for Medical AI Contest 2024 (https://www.kaggle.com/competitions/medical-ai-contest2024/submissions)

  Language:Jupyter Notebook3

• tansel/deepmapper

  DeepMapper enables a simple pipeline to process non-image data as images to analyse any high dimensional data using CNNs or various DL algorithms and systemically collect and interpret results

  Language:Jupyter Notebook3200

• DIAGNijmegen/LEOPARD-challenge-baseline

  Language:Python130