This repo is the official implementation of paper "Automated Lesion Segmentation in Fundus Images with Many-to-Many Reassembly of Features".
This code is based on mmsegmentation.
- pytorch=1.6.0
- mmsegmentation=0.8.0
- mmcv=1.2.0
conda create -n m2mrf python=3.7 -y
conda activate m2mrf
conda install pytorch=1.6.0 torchvision cudatoolkit=10.2 -c pytorch -y
pip install mmcv-full==1.2.0 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.6.0/index.html -i https://pypi.douban.com/simple/
pip install opencv-python
pip install scipy
pip install tensorboard tensorboardX
pip install sklearn
pip install terminaltables
pip install matplotlib
cd M2MRF-Lesion-Segmentation
chmod u+x tools/*
pip install -e .
# prepare dataset
python tools/prepare_labels.py
python tools/augment.py
# train
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=12345 tools/dist_train.sh configs/m2mrf/fcn_hr48-M2MRF-C_40k_idrid_bdice.py 4
# test
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=12345 tools/dist_test.sh configs/m2mrf/fcn_hr48-M2MRF-C_40k_idrid_bdice.py /path/to/fcn_hr48-M2MRF-C_40k_idrid_bdice_iter_40000.pth 4 --eval mIoU
We evaluate our method on IDRiD and DDR.
| method | mIOU | mAUPR | download |
|---|---|---|---|
| M2MRF-A | 49.86 | 67.15 | config | model |
| M2MRF-B | 49.33 | 66.71 | config | model |
| M2MRF-C | 50.17 | 67.55 | config | model |
| M2MRF-D | 49.96 | 67.32 | config | model |
| method | mIOU | mAUPR | download |
|---|---|---|---|
| M2MRF-A | 31.47 | 49.56 | config | model |
| M2MRF-B | 30.56 | 49.86 | config | model |
| M2MRF-C | 30.39 | 49.20 | config | model |
| M2MRF-D | 30.76 | 49.47 | config | model |
In the paper, we reported average performance over three repetitions, but our code only reported the best one among them.
If you find this code useful in your research, please consider citing:
@article{liu2023m2mrf,
title = {Automated Lesion Segmentation in Fundus Images with Many-to-Many Reassembly of Features},
author = {Qing Liu and Haotian Liu and Wei Ke and Yixiong Liang},
journal = {Pattern Recognition},
volume = {136},
pages = {109191},
year = {2023},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2022.109191},
}