iBrain-007

iBrain-007's Stars

• MikhailKulyabin/OCTDL

  Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

  Language:Python251

• agaldran/lwnet

  State-of-the-art retinal vessel segmentation with minimalistic models

  Language:Python7927

• pkumivision/FFC

  This is an official pytorch implementation of Fast Fourier Convolution.

  Language:Python36232

• caojiezhang/CiaoSR

  PyTorch implementation of "CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution (CVPR2023)"

  Language:Python1203

• saeed-anwar/DRLN

  Densely Residual Laplacian Super-resolution, IEEE Pattern Analysis and Machine Intelligence (TPAMI), 2020

  Language:Python18434

• seung-lab/euclidean-distance-transform-3d

  Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.

  Language:C++24936

• Kent0n-Li/Medical-Image-Segmentation

  MedSeg: Medical Image Segmentation GUI Toolbox 可视化医学图像分割工具箱

  Language:Python22319

• BRAINSia/BRAINSTools

  A suite of tools for medical image processing focused on brain analysis

  Language:C++11797

• adamkwolf/3d-nii-visualizer

  A NIfTI (nii.gz) 3D Visualizer using VTK and Qt5

  Language:Python17032

• YueCui-Labs/FFCM-MRF

  MATLAB GUIs for ToF-MRA data preprocessing, cerebrovascular segmentation and quantification

  Language:MATLAB143

• ReVanced/revanced-patches

  🧩 Patches for ReVanced

  Language:Kotlin3k341

• pyvista/tetgen

  A Python interface to the C++ TetGen library to generate tetrahedral meshes of any 3D polyhedral domains

  Language:C++23633

• junyuchen245/Transformer_for_medical_image_analysis

  A collection of papers about Transformer in the field of medical image analysis.

  46770

• CityU-AIM-Group/SFDA-FSM

  [MIA' 22] Source free domain adaptation for medical image segmentation with fourier style mining

  Language:Python438

• MASILab/UNesT

  The official Pytorch implementation of UNesT

  Language:Python214

• DeepMed-Lab-ECNU/BCP

  Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation (CVPR 2023)

  Language:Python17116

• mavillan/py-hausdorff

  Fast computation of Hausdorff distance in Python

  Language:Python25234

• jeya-maria-jose/UNeXt-pytorch

  Official Pytorch Code base for "UNeXt: MLP-based Rapid Medical Image Segmentation Network", MICCAI 2022

  Language:Python49977

• inducer/meshpy

  2D/3D simplicial mesh generator interface for Python (Triangle, TetGen, gmsh)

  Language:C++539110

• rmaphoh/RETFound_MAE

  RETFound - A foundation model for retinal image

  Language:Python40398

• McGregorWwww/UCTransNet

  Implementation of our AAAI'22 work: 'UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer'.

  Language:Python39749

• yaoyao-liu/meta-transfer-learning

  TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

  Language:Python757152

• JunMa11/SegLossOdyssey

  A collection of loss functions for medical image segmentation

  Language:Python3.9k604

• DigitalSlideArchive/HistomicsTK

  A Python toolkit for pathology image analysis algorithms.

  Language:Python410117

• qurit/rt-utils

  A minimal Python library to facilitate the creation and manipulation of DICOM RTStructs.

  Language:Python22260

• 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:Python30129

• kaidic/LDAM-DRW

  [NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

  Language:Python660117

• wasidennis/AdaptSegNet

  Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

  Language:Python852205

• vandit15/Class-balanced-loss-pytorch

  Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

  Language:Python790121

• xiangruili/dicm2nii

  DICOM to NIfTI conversion, NIfTI tools

  Language:MATLAB9443