RMLoss: Regression Metric Loss

RMLoss explores the structure of the continuous label space and regularizes the model to learn a better representation space which is a semantically meaningful manifold that is isometric to the label space. The paper has been accepted by MICCAI 2022.

Prerequisites

Python 3.8
PyTorch 1.8.2+
A computing device with GPU

Getting started

Installation

(Not necessary) Install Anaconda3
Install CUDA 11.0+
Install PyTorch 1.8.2+

Noted that our code is tested based on PyTorch 1.8.2

Dataset & Preparation

The original RSNA Pediatric Bone Age Dataset contains various noises. In our experiments, we used preprocessed data from this repository. All images are resized into 400x520.

The trained model is at ./work/checkpoints
The data splition used in our experiments is at ./work/data/data_info.csv
Before running the code, please put the preprocessed images into ./work/data/img

Train

Train a model by

python train_main.py

Evaluation

Evaluate the trained model by

python test_main.py

--iter iteration of the checkpoint to load. #Default: 14500
--batch_size batch size of the parallel test. #Default: 64

Citation

Please cite these papers in your publications if it helps your research:

@article{chao2022regression,
  title={Regression Metric Loss: Learning a Semantic Representation Space for Medical Images},
  author={Chao, Hanqing and Zhang, Jiajin and Yan, Pingkun},
  journal={arXiv preprint arXiv:2207.05231},
  year={2022}
}

Link to paper:

Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

License

The source code of RMLoss is licensed under a MIT-style license, as found in the LICENSE file. This code is only freely available for non-commercial use, and may be redistributed under these conditions. For commercial queries, please contact Dr. Pingkun Yan.

DIAL-RPI/Regression-Metric-Loss