Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation

This is the implementation of our puplication in IEEE Transactions on Biomedical Engineering. This work is implemeneted in PyTorch and more specifically PyTorch Lightning.

Objective: The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce such efforts are desired. Methods: Self-Supervised Learning (SSL) is an emerging field that increases performance when unannotated data is present. However, profound studies regarding segmentation tasks and small datasets are still absent. A comprehensive qualitative and quantitative evaluation is conducted, examining SSL's applicability with a focus on biomedical imaging. We consider various metrics and introduce multiple novel application-specific measures. All metrics and state-of-the-art methods are provided in a directly applicable software package. Results: We show that SSL can lead to performance improvements of up to 10%, which is especially notable for methods designed for segmentation tasks. Conclusion: SSL is a sensible approach to data-efficient learning, especially for biomedical applications, where generating annotations requires much effort. Additionally, our extensive evaluation pipeline is vital since there are significant differences between the various approaches. Significance: We provide biomedical practitioners with an overview of innovative data-efficient solutions and a novel toolbox for their own application of new approaches. Our pipeline for analyzing SSL methods is provided as a ready-to-use software package.

Project Structure

Overview of this repository:

.
├── DLIP
│   ├── data #  Contains the defined datasets as PyTorch Lightning DataModules & Datasets.   
│   ├── experiments #  Contains experiment configurations as yaml files.
│   ├── models #  Contains the defined models as PyTorch Modules.    
│   ├── objectives #  Contains the defined objectives as PyTorch Modules.
│   ├── scripts #  Contains the training and inference scripts.
│   └── utils #  Contains utils functions, which can be used by all modules.

The training (DLIP/scripts/train.py) and inference script (DLIP/scripts/inference.py) are configured by the defined experiments (DLIP/experiments) and utilize the defined datamodules (DLIP/data), models (DLIP/models) and objectives (DLIP/objectives).

Install

Prerequisite

Python == 3.8.5
Pip == 21.2.4

Conda Environment

conda create --name YOUR_ENV_NAME -f snv_ssl.yml

Pip Installation