WentianZhang-ML/GatingAno

This is an official pytorch implementation of 'Anomaly Detection via Gating Highway Connection for Retinal Fundus Images'.

Python

GatingAno

This is an official pytorch implementation of 'Anomaly Detection via Gating Highway Connection for Retinal Fundus Images'.

Requirements

numpy>=1.17.0
scipy>=1.5.2
Pillow>=8.2.0
pytorch>=1.7.1
torchvision>=0.8.2
tqdm>=4.59.0
scikit-learn>= 0.24.2
scikit-image>=0.17.2

Datasets

The proposed method is evaluated on two publicly-available datasets, i.e.

IDRiD
ADAM

Usage

The proposed GatingAno method is trained through two steps:

Data Preparation

Generate the list of HOG image and Patches :
```
python3 data_find.py \
--dataset ['IDRiD'/'IDRiDc'/'ADAM'/'ADAMc'] \
--path {data dir}
```
For example, to generate the image-level label of IDRiD dataset, you can run python3 data_find.py --dataset 'IDRiDc' --path './dataset/'

And then you will get lists containing images and corresponding labels in './label/IDRiDc/'.
Training and testing model

For example, to train pixel-level anomaly detection task on ADAM dataset, you can run
```
python3 main.py \
--dataset 'ADAM' \
--datadir './labels/ADAM/' \
--lr 1e-3 \
--level 'pixel' ;
```