xy0806

xy0806's Stars

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook46.8k3066625.5k

• Beckschen/TransUNet

  This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

  Language:Python2.3k12150495

• HiLab-git/SSL4MIS

  Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

  Language:Python2.1k3786382

• zhoubolei/CAM

  Class Activation Mapping

  Language:MATLAB1.8k4651466

• wasserth/TotalSegmentator

  Tool for robust segmentation of >100 important anatomical structures in CT and MR images

  Language:Python1.4k27327238

• dusty-nv/ros_deep_learning

  Deep learning inference nodes for ROS / ROS2 with support for NVIDIA Jetson and TensorRT

  Language:C++88436112258

• MontaEllis/Pytorch-Medical-Segmentation

  This repository is an unoffical PyTorch implementation of Medical segmentation in 2D and 3D.

  Language:Python8511151196

• SeedV/generative-ai-roadmap

  The roadmap of generative AI: use cases and applications | 生成式AI的应用路线图

  59328272

• Project-MONAI/MONAILabel

  MONAI Label is an intelligent open source image labeling and learning tool.

  Language:Python59124520189

• DeepRegNet/DeepReg

  Medical image registration using deep learning

  Language:Python5641739776

• Tencent/deepx_core

  deepx_core是一个专注于张量计算/深度学习的基础库

  Language:C++37018554

• Ainimal/Aini_Medic

  A simple-to-use yet function-rich medical image processing toolbox

  Language:Python20012128

• xianlin7/SAMUS

  Language:Python14312513

• 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:Python13651716

• Jeff-sjtu/labelKeypoint

  Keypoints label tools

  Language:Python884625

• Phoenix1153/ViT_OOD_generalization

  Language:Python84336

• fkong7/MeshDeformNet

  A deep-learning approach for direct whole-heart mesh reconstruction

  Language:Python8111514

• cherise215/advchain

  [Medical Image Analysis] Adversarial Data Augmentation with Chained Differentiable Transformations (AdvChain)

  Language:Jupyter Notebook57227

• med-air/Contrastive-COVIDNet

  [IEEE JBHI'20] Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification

  Language:Python544617

• paint4poem/paint4poem

  Code for paintt4poem paper

  Language:Python50145

• easylearn-fmri/easylearn_dev

  Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatric diseases or other diseases, disease mechanism exploration and etc.

  Language:Python364216

• XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease

  31401

• JunMa11/HM_DataAug

  A solution to MICCAI 2020 M&Ms

  Language:Python25324

• XiaoweiXu/Dataset_Type-B-Aortic-Dissection

  25241

• XiaoweiXu/Whole-heart-and-great-vessel-segmentation-of-chd_segmentation

  A dataset of whole heart and great vessel segmentation of chd_segmentation is published.

  25233

• ykl-ucla/prostate_zonal_seg

  This is a DL-based algorithm for the prostate zonal segmentation

  Language:Python245

• ArnaoutLabUCSF/cardioML

  Arnaout Lab

  Language:Python19337

• heyufan1995/self-domain-adapted-network

  MICCAI2020: self domain adapted network

  Language:Python17361

• znyan/iMOS

  Language:Python31

• wd111624/CMR_plan

  Language:Python2