601SunG

Awesome-Learning-with-Label-Noise

A curated list of resources for Learning with Noisy Labels

BCDU-Net

BCDU-Net : Medical Image Segmentation

BEAL

code for paper Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

brainSPADE_RELEASE

CaraNet

Context Axial Reverse Attention Network for Small Medical Objects Segmentation

cdsb

CE-Net

The manuscript has been accepted in TMI.

cleanlab

The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models.

Confident_Learning_for_Noisy-labeled_Medical_Image_Segmentation

601SunG/PreProcPipe

601SunG/Diffusion-Models-pytorch

Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)

601SunG/segformer-pytorch

这是一个segformer-pytorch的源码，可以用于训练自己的模型。

601SunG/EATA

Code for ICML 2022 paper — Efficient Test-Time Model Adaptation without Forgetting

601SunG/conformal-prediction

Lightweight, useful implementation of conformal prediction on real data.

601SunG/diffusion-models-tutorial

Experiment with diffusion models that you can run on your local jupyter instances

601SunG/brainSPADE_RELEASE

601SunG/cdsb

601SunG/SegFormer

Implementation of SegFormer in PyTorch

601SunG/UNeXt-pytorch

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

601SunG/SynthSR

A framework for joint super-resolution and image synthesis, without requiring real training data

601SunG/nnUNet

601SunG/UCTransNet

601SunG/SFDA

Source-Free Domain Adaptation

601SunG/CaraNet

Context Axial Reverse Attention Network for Small Medical Objects Segmentation

601SunG/MCTrans

601SunG/SGL-Retinal-Vessel-Segmentation

[MICCAI 2021] Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels: New SOTA on both DRIVE and CHASE_DB1.

601SunG/Swin-Unet

The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

601SunG/S-cuda

Code for S-cuda: Self-Cleansing Unsupervised Domain Adaptation for Medical Image Segmentation

601SunG/TransBTS

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf) , accepted by MICCAI2021.

601SunG/MetaCorrection

[CVPR'21] MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation

601SunG/Medical-Transformer

Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021

601SunG/segtran

Medical Image Segmentation using Squeeze-and-Expansion Transformers

601SunG/MixDANN

This is the code for the paper "Robust White Matter Hyperintensity Segmentation on Unseen Domain"

601SunG/cleanlab

The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models.

601SunG/Confident_Learning_for_Noisy-labeled_Medical_Image_Segmentation

601SunG/Awesome-Learning-with-Label-Noise

A curated list of resources for Learning with Noisy Labels

601SunG/boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

601SunG/confidentGAN

601SunG/KiU-Net-pytorch

Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)