FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
This addresses the challenge of expensive and time-consuming expert annotations in medical image segmentation, though it is incremental as it adapts an existing method to a new task.
The authors tackled the problem of semi-supervised semantic segmentation in medical imaging by adapting the FixMatch method, showing that FixMatchSeg performs on par with strong supervised baselines when few labels are available.
Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years.However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming.Semi-supervised segmentation can be an attractive solution where a very few labeled images are used along with a large number of unlabeled ones. While the gap between supervised and semi-supervised methods have been dramatically reduced for classification problems in the past couple of years, there still remains a larger gap in segmentation methods. In this work, we adapt a state-of-the-art semi-supervised classification method FixMatch to semantic segmentation task, introducing FixMatchSeg. FixMatchSeg is evaluated in four different publicly available datasets of different anatomy and different modality: cardiac ultrasound, chest X-ray, retinal fundus image, and skin images. When there are few labels, we show that FixMatchSeg performs on par with strong supervised baselines.