AI4Eyes

This repository contains the official implementation for "Multi-level diagnosis of cataract from anterior images via deep learning".

Deployment Demo

You can access the AI-guided diagnosis models when uploading an anterior image through edge devices (i.e. smartphone, tablet and laptop). Here is the demo link: http://eye.masaikk.icu

binary diagnosis	three-level diagnosis	four-level diagnosis

Installation

Tested on: Ubuntu 20 with torch 2.0 & CUDA 11.8 on an A100.
Windows 10 with torch 1.10 & CUDA 10.2 on a GTX-1650.

conda create -n ai4eyes python==3.8
# Ubuntu 20 with an A100
pip install torch==2.0.0+cu118 torchvision==1.15.0+cu118
# Windows 10 on a GTX-1650
pip install torch==1.10.0+cu102 torchvision==0.11.0+cu102

Data pre-processing

Split into train / val / test

cd misc
python pre_proces.py --img-dir cataract_org --out-dir cataract_img

Training

To facilitate AI-guided multilevel diagnosis, three deep learning models (i.e. binary, three-level classification and four-level classification model) have been trained.

python train.py --out-dir output_path --batch-size 64 --inet-pretrain

Test

To test the performance of trained model, load the saved checkpoints which we have provided in the checkpoints folds.

new_net = models.resnet18(pretrained=args.inet_pretrain)
new_net.fc = nn.Linear(512, args.nb_cls)
new_net.load_state_dict(torch.load('net_cataract.pt'))

t-Distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE is used for visualization and exploratory data analysis of deep learning models.

python t-SNE.py

Class Activation Mapping (CAM)

CAM is a technique used in computer vision to visualize and understand the important regions of an image that contribute to the classification decision made by a deep learning model.

python CAM.py

wuxianjia1996/AI4Eyes