huanqingxu

3D-ResNets-PyTorch

3D ResNets for Action Recognition (CVPR 2018)

DANet

Dual Attention Network for Scene Segmentation (CVPR2019)

U2PL

[CVPR'22] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

PSANet

PSANet: Point-wise Spatial Attention Network for Scene Parsing, ECCV2018.

segtran

Medical Image Segmentation using Squeeze-and-Expansion Transformers

CrossViT-pytorch

Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

semisup-semseg

DST-CBC

Implementation of our Pattern Recognition paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DAG4MIA

Domain Adaptation and Generalization for Medical Image Analysis

U-Net-Transformer

COPLE-Net

COVID-19 Pneumonia Lesion segmentation network

GBDL

A Pytorch implementation of CVPR 2022 paper "Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation"

pseudo_label_contrastive_training

Code for Pseudo label based contrastive learning joint training approach

CLD-Semi

MICCAI 2022: Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

SSL-For-Medical-Segmentation

This repository is an unoffical PyTorch implementation of Semi-Supervised Learning on Medical Segmentation in 2D and 3D.

BayeSR

The official implementation of "Bayesian Image Super-Resolution with Deep Modeling of Image Statistics" via TensorFlow

SSNS-Net

Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning

Self-paced-Contrastive-Learning

Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels for neurips 2021

pytorch_3D_medical_classification

pytorch application for 3D classification using medical images

CASF-Net

CASF-Net: Cross-attention And Cross-scale Fusion Network for Medical Image Segmentation (Submitted)

s5cl

S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning

DL_Radiomics_Fusion

Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scan

SCC

[CMIG‘2022] [Pytorch]A Contrastive Consistency Semi-supervised Left Atrium Segmentation Model

MASS

MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images

RMT_VAT

SD-LayerNet

MPP

M3DV-Medical-Image-Classification

Resnet 3D

Deep-Learning-Radiomics-Research-in-Breast-Cancer

Camptothecin-can-increase-the-infiltration-of-CD8-T-cells-in-local-rabbit-VX2-liver-tumor

This study used the method of network pharmacology to study the inhibitory effect of camptothecin on HSP90β protein by mining the database of traditional Chinese medicine and molecular structure database, and initially verified it in a rabbit liver cancer model.