This is an implementation of MICCAI 2019 paper MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection.
This code is based on Detectron.pytorch. Please see it for installation.
- Python (Tested on 3.6)
- PyTorch (Tested on 0.4.1.post2)
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
│ ├── ...
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.
After training, put the model path into .sh file, after '--load_ckpt', and run:
bash multi_windows_9_slices.sh test
| 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.
If you have questions or suggestions, please open an issue here or send an email to [email protected].