zonechen1994

zonechen1994's Stars

• Significant-Gravitas/AutoGPT

  AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

  Language:Python168k1.5k2.8k44.3k

• fendouai/PyTorchDocs

  PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！

  Language:Python2.5k565700

• napari/napari

  napari: a fast, interactive, multi-dimensional image viewer for python

  Language:Python2.2k463k421

• taokz/BiomedGPT

  BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks

  Language:Python520195459

• Qinying-Liu/Awesome-Open-Vocabulary-Semantic-Segmentation

  A curated publication list on open vocabulary semantic segmentation and related area (e.g. zero-shot semantic segmentation) resources..

  43316422

• prov-gigapath/prov-gigapath

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

  Language:Python408410654

• maduc7/Histopathology-Datasets

  Ressources of histopathology datasets

  2658343

• Imageomics/bioclip

  This is the repository for the BioCLIP model and the TreeOfLife-10M dataset [CVPR'24 Oral, Best Student Paper].

  Language:Python162141013

• Lee-Gihun/MEDIAR

  (NeurIPS 2022 CellSeg Challenge - 1st Winner) Open source code for "MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy"

  Language:Python14151930

• PathologyDataScience/BCSS

  Use this to download all elements of the BCSS dataset described in: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

  Language:Python14051716

• ksugar/qupath-extension-sam

  QuPath extension for Segment Anything Model (SAM)

  Language:Java9121413

• tianshilu/pMTnet

  Deep Learning the T Cell Receptor Binding Specificity of Neoantigen

  Language:Python7632120

• ruppinlab/PERCEPTION

  Utilizing single-cell omics from patients tumor to predict response and resistance.

  615515

• vanvalenlab/cellSAM

  Codebase for "A Foundation Model for Cell Segmentation"

  Language:Jupyter Notebook514157

• FT-ZHOU-ZZZ/CMTA

  Here is the official implementation of the paper "Cross-Modal Translation and Alignment for Survival Analysis"

  Language:Python46155

• wukevin/tcr-bert

  Large language modeling applied to T-cell receptor (TCR) sequences.

  Language:Jupyter Notebook461118

• mnielLab/NetTCR-2.0

  NetTCR-2.0. Sequence-based prediction of peptide-TCR binding

  Language:Python3331110

• MASILab/pyPheWAS

  MASI Lab Implementation of PheWAS in Python

  Language:Python27743

• cpystan/WSI-VQA

  [ECCV 2024] Official Implementation of 《WSI-VQA: Interpreting Whole Slide Image by Generative Question Answering》

  Language:Python24350

• bmi-imaginelab/SI-MIL

  SI-MIL

  Language:Python212

• BearCleverProud/MoME

  Repository for Mixture of Multimodal Experts

  Language:Python19202

• BiomedicalMachineLearning/stlearn_interactive

  Interactive applications of stlearn

  Language:Python15350

• yjdeng9/DeepMSProfiler

  DeepMSProfiler, an innovative data analysis tool focused on liquid chromatography mass spectrometry (LC-MS) data.

  Language:Python122

• KatherLab/highres-WSI-heatmaps

  Language:Python7205

• bm2-lab/UniTCR

  Language:Python6600

• BiomedicalMachineLearning/stlearn_manuscript

  Codes for generating stlearn figures

  Language:Jupyter Notebook5121

• SpatialTranscriptomicsResearch/st_analysis

  A toolkit for visualisation and analysis of Spatial Transcriptomics datasets

  Language:Python5315

• enkim/IMC-IF

  Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation

  Language:Jupyter Notebook42

• Bioinformatics7181/DeepLION

  Deep MuLti-Instance Learning Improves the Prediction of Cancer-associated T Cell ReceptOrs for Accurate CaNcer Detection.

  Language:Python3111

• JiangBioLab/iCanTCR

  a deep learning framework for early cancer detection using T cell receptor repertoire in peripheral blood

  Language:Python2200