Pinned Repositories
Andrew-Ng-Deep-Learning-notes
吴恩达《深度学习》系列课程笔记及代码
Andrew_Ng_DL_Assignment
微专业: 吴恩达 深度学习工程师 作业
awesome-federated-learning
A collection of AWESOME things about federated learning.
DCDA
FedICRA
The official implementation of the paper "Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation".
LovaszSoftmax
Code for the Lovász-Softmax loss (CVPR 2018)
MultitaskOCTA
This repository is an official PyTorch implementation of the paper "BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images", MICCAI 2021.
The-SUSTech-SYSU-dataset-for-automated-exudate-detection-and-diabetic-retinopathy-grading
Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: leftversus- right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.
VesselSeg-Pytorch
Retinal vessel segmentation toolkit based on pytorch
YoloCurvSeg
[MedIA'23] "YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation".
llmir's Repositories
llmir/FedICRA
The official implementation of the paper "Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation".
llmir/MultitaskOCTA
This repository is an official PyTorch implementation of the paper "BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images", MICCAI 2021.
llmir/YoloCurvSeg
[MedIA'23] "YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation".
llmir/The-SUSTech-SYSU-dataset-for-automated-exudate-detection-and-diabetic-retinopathy-grading
Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: leftversus- right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.
llmir/VesselSeg-Pytorch
Retinal vessel segmentation toolkit based on pytorch
llmir/awesome-federated-learning
A collection of AWESOME things about federated learning.
llmir/BBR-Net
llmir/DCDA
llmir/RVms
llmir/ComboLoss
llmir/CPFNet_Project
This is a pytorch project about medical segmentation
llmir/D-UNet
llmir/da-sac
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)
llmir/DANet
Dual Attention Network for Scene Segmentation (CVPR2019)
llmir/DetectoRS
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
llmir/FedDG-ELCFS
[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
llmir/focusnet-alpha
Code for Focusnet (ISBI2019) and FocusnetAlpha
llmir/IterNet
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. High-accuracy medical retina (eye) image segmentation.
llmir/Masking-Augmentation-Opencv-Keras-Image-Processing
Mask creation for segmentation task using opencv and Image augmentation tasks with imgaug library
llmir/Patho-GAN
Patho-GAN: interpretation + medical data augmentation. Code for paper work "Explainable Diabetic Retinopathy Detection and Retinal Image Generation"
llmir/PC-CHiP
Pan-Cancer Computational Histopathology
llmir/prepare_detection_dataset
convert dataset to coco/voc format
llmir/pthread-tutorial-1
llmir/py-hausdorff
Fast computation of Hausdorff distance in Python
llmir/recurrent-unet
ICCV 2019: Recurrent U-Net for Resource Constraint Segmentation
llmir/SA-UNet
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.
llmir/self_supervised
llmir/SeqNet
Joint Learning of Vessel Segmentation and Artery/Vein Classification
llmir/toolbox
various tools such as label-tools , data-augmentation , etc.
llmir/yolov5
YOLOv5 in PyTorch > ONNX > CoreML > iOS