Pinned Repositories
3D_Attention_UNet
3DFasterRCNN_LungNoduleDetector
3DShapeContexts
Accompanying source code for my 2003 paper 3D Shape Matching with 3D Shape Contexts
3DUnetCNN
Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
awesome-semi-supervised-learning
:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
Awesome-Vision-Attentions
Summary of related papers on visual attention. Related code will be released based on Jittor gradually.
Deep-Reinforced-Tree-Traversal
abcxubu's Repositories
abcxubu/SegLoss
A collection of loss functions for medical image segmentation
abcxubu/SegWithDistMap
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
abcxubu/SLATracker
Spatial-Attention Location-Aware Multi-Object Tracking
abcxubu/TransUNet
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
abcxubu/CoTr
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
abcxubu/self-attention-cv
Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.
abcxubu/AdelaiDet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
abcxubu/Medical-Transformer
Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"
abcxubu/TransBTS
This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).
abcxubu/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.
abcxubu/U-2-Net
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
abcxubu/Loss_ToolBox-PyTorch
PyTorch Implementation of Focal Loss and Lovasz-Softmax Loss
abcxubu/ViT-pytorch
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
abcxubu/MedicalDataAugmentationTool-MMWHS
abcxubu/u2net_torch
MICCAI2019:3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
abcxubu/Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
abcxubu/UA-MT
code for MICCAI 2019 paper 'Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation'.
abcxubu/awesome-semi-supervised-learning
:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
abcxubu/mean-teacher
A state-of-the-art semi-supervised method for image recognition
abcxubu/deepvesselnet
Implementation of the DeepVesselNet deep learning network
abcxubu/CoronaryCenterlineUnet
abcxubu/SimpleCRF
matlab and python wrap of crf and dense crf, both 2d and 3d are supported
abcxubu/HausdorffLoss
Implementation of Hausdorff loss function for DNN learning in segmentation tasks.
abcxubu/SVS-net
Sequential vessel segmentation via deep channel attention network
abcxubu/tf_unet
Generic U-Net Tensorflow implementation for image segmentation
abcxubu/NiftyNet
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
abcxubu/pytorch-nested-unet
PyTorch implementation of UNet++ (Nested U-Net).
abcxubu/awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
abcxubu/bcpd
Bayesian coherent point drift for Windows 10
abcxubu/PRMLT
Matlab code for machine learning algorithms in book PRML