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README.md

MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection

This is an implementation of MICCAI 2019 paper MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection.

Installation

This code is based on Detectron.pytorch. Please see it for installation.

Environment

  • Python (Tested on 3.6)
  • PyTorch (Tested on 0.4.1.post2)

Data preparation

Download DeepLesion dataset here.

We provide coco-style json annotation files converted from DeepLesion. Unzip Images_png.zip and make sure to put files as following sturcture:

data
  ├──DeepLesion
        ├── annotations
        │   ├── deeplesion_train.json
        │   ├── deeplesion_test.json
        │   ├── deeplesion_val.json
        └── Images_png
              └── Images_png
               │    ├── 000001_01_01
               │    ├── 000001_03_01
               │    ├── ...

Training

To train MVP-Net with 9 slices model, run:

bash multi_windows_9_slices.sh train

We also provide our re-implementation of 3DCE, see 3DCE_*.sh for training and testing.

Testing

After training, put the model path into .sh file, after '--load_ckpt', and run:

bash multi_windows_9_slices.sh test

Results on DeepLesion dataset

FPs per image 0.5 1 2 3 4
ULDOR 52.86 64.80 74.84 - 84.38
3DCE, 3 slices 55.70 67.26 75.37 - 82.21
3DCE, 9 slices 59.32 70.68 79.09 - 84.34
3DCE, 27 slices 62.48 73.37 80.70 - 85.65
FPN+3DCE, 3 slices* 58.06 68.85 77.48 81.03 83.27
FPN+3DCE, 9 slices* 64.25 74.41 81.90 85.02 87.21
FPN+3DCE, 27 slices* 67.32 76.34 82.90 85.67 87.60
Ours, 3 slices 70.01 78.77 84.71 87.58 89.03
Ours, 9 slices 73.83 81.82 87.60 89.57 91.30
Imp over 3DCE, 27slices 11.35 8.45 6.90 - 5.65

* indicates our re-implementation of 3DCE with FPN as backbone.

Contact

If you have questions or suggestions, please open an issue here or send an email to lizihao2018@ia.ac.cn.

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An implementation of MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection.

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