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LEI SHI's Projects

attention-gated-networks icon attention-gated-networks

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

axial-attention icon axial-attention

Implementation of Axial attention - attending to multi-dimensional data efficiently

canet icon canet

TMI 19: CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

classifier-balancing icon classifier-balancing

This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 2020

deit icon deit

Official DeiT repository

dip icon dip

数字图像处理 冈萨雷斯 第四版

dr-segmentation icon dr-segmentation

Code Repo for Lesion Segmentation for Diabetic Retinopathy with Adversarial Learning

idrid-challenge icon idrid-challenge

Automatic grading, segmentation and detection for IDRiD Diabetic Retinopathy dataset. (MVA Medical Imaging class final project)

image_segmentation icon image_segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

ldam-drw icon ldam-drw

[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

levit icon levit

LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

medical-transformer icon medical-transformer

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

pretrained-models.pytorch icon pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

pytorch-3dunet icon pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

pytorch-image-models icon pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, DPN, CSPNet, and more

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