Which images to label for few-shot medical landmark detection?
python == 3.5/3.6,
pytorch >= 1.1.0,
torchvison >= 0.6
We train/test our model on Cephalometric Dataset
We expect the directory structure to be the following:
path/to/cephalometric
400_junior
001.txt
...
400_senior
001.txt
...
RawImage
TrainingData
001.bmp
...
Test1Data
151.bmp
...
Test2Data
301.bmp
...
- Train the feature extractor
python -m sc.ssl.ssl --tag run- extract SIFT key points
python -m sc.select.sift_select --tag sift- calculate similarities (Respective score)
python -m sc.select.maxsim_sift --tag sift- select templates
python -m sc.select.selct_ids --tag sift
- Estimate all MRE
python -m sc.select.test_by_multi --tag xx
- Test templates
python -m sc.select.test_specific_ids --indices xxx
This code is released under the Apache 2.0 license. Please see the LICENSE file for more information.
@InProceedings{Quan_2022_CVPR,
author = {Quan, Quan and Yao, Qingsong and Li, Jun and Zhou, S. Kevin},
title = {Which Images To Label for Few-Shot Medical Landmark Detection?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {20606-20616}
}