/WDMT-Net

This is an official implementation of 'A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images'

Primary LanguagePythonApache License 2.0Apache-2.0

WDMT-Net

This is an official implementation of 'A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images'. (Accepted by MICCAI 2022)

A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images

Method

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.

Usage

The proposed WDMT-Net method is trained through two steps:

  • Data Preparation

    Generate the list of HOG image and Patches :

    python3 data_find.py \
--dataset ['IDRiD'/'ADAM'] \
--path {data dir}

    For example, python3 data_find.py --dataset 'IDRiD' --path './dataset/'

    And then you can get lists containing images and corresponding labels in './label/'.

  • Training and testing model

    python3 main.py \
--dataset ['IDRiD'/'ADAM'] \
--datadir './labels/' \
--hog \
--lr 1e-4 \
--batch_size 32 \
--epochs 200 \
--deta 0.05 \

Visualization

Visual