Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation

TL;DR

This paper introduces an automated pipeline that leverages foundation models like SAM and MedSAM to generate weak labels for unlabeled medical images, enhancing training datasets for segmentation tasks in various medical imaging modalities.

Contribution

The proposed method automates weak label generation for medical images using foundation models, reducing manual effort and enabling effective training with limited labeled data.

Findings

Effective in label-scarce settings across multiple modalities

Automates label generation for real and synthetic images

Improves segmentation performance with weakly labeled data

Abstract

The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks. In this work, we present a new approach to overcome the hurdle of costly medical image labeling by leveraging foundation models like Segment Anything Model (SAM) and its medical alternate MedSAM. Our pipeline has the ability to generate weak labels for any unlabeled medical image and subsequently use it to augment label-scarce datasets. We perform this by leveraging a model trained on a few gold-standard labels and using it to intelligently prompt MedSAM for weak label generation. This automation eliminates the manual prompting step in MedSAM, creating a streamlined process for generating labels for both real and synthetic images, regardless of quantity. We conduct experiments on label-scarce settings for multiple tasks pertaining to modalities ranging from ultrasound, dermatology, and X-rays to demonstrate the usefulness of our pipeline. The code is available at https://github.com/stanfordmlgroup/Auto-Generate-WLs/.