Pinned Repositories
CVPR2023-Paper-Code-Interpretation
cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
hello-world
just another description
lightly
A python library for self-supervised learning on images.
LLMs-from-scratch
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
QIN-PROSTATE-Repeatability-Radiomics
Retinal-lesion-Segmentation
Diabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions helps early diagnosis of DR. However, due to the scarcity of pixel-level annotations and the large diversity between different types of DR lesions, the existing deep learning methods are very challenging in performing segmentation on retinal images. In this study, we propose a novel data augmentation method based on Poisson-blending (PB) algorithm to generate synthetic images, which can be easily adapted to other medical anomaly segmentation tasks to alleviate the training data scarcity issue. We also proposed a CNN architecture for the simultaneous segmentation of multiscale anomaly signs. The performances are compared with the state-of-the-art methods on Indian Diabetic Retinopathy Image Dataset (IDRiD) and e-ophtha datasets, both widely used in the research community. The results indicate that the proposed method significantly outperforms the state-of-the-art methods.
test-20231221
WORC
Workflow for Optimal Radiomics Classification
fzhan007's Repositories
fzhan007/CVPR2023-Paper-Code-Interpretation
cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理
fzhan007/hello-world
just another description
fzhan007/lightly
A python library for self-supervised learning on images.
fzhan007/LLMs-from-scratch
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
fzhan007/ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
fzhan007/QIN-PROSTATE-Repeatability-Radiomics
fzhan007/Retinal-lesion-Segmentation
Diabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions helps early diagnosis of DR. However, due to the scarcity of pixel-level annotations and the large diversity between different types of DR lesions, the existing deep learning methods are very challenging in performing segmentation on retinal images. In this study, we propose a novel data augmentation method based on Poisson-blending (PB) algorithm to generate synthetic images, which can be easily adapted to other medical anomaly segmentation tasks to alleviate the training data scarcity issue. We also proposed a CNN architecture for the simultaneous segmentation of multiscale anomaly signs. The performances are compared with the state-of-the-art methods on Indian Diabetic Retinopathy Image Dataset (IDRiD) and e-ophtha datasets, both widely used in the research community. The results indicate that the proposed method significantly outperforms the state-of-the-art methods.
fzhan007/test-20231221
fzhan007/WORC
Workflow for Optimal Radiomics Classification