Consistent Semi-Supervised, Explainable Multi-Tasking for Medical Imaging

MultiMix: Sparingly Supervised, Extreme Multitask Learning from Medical Images

Schematic of Our Proposed Model: MultiMix (Image by Author)

In this article, I will discuss a new semi-supervised, multi-tasking medical imaging method called MultiMix, by Ayaan Haque (me), Abdullah-Al-Zubaer Imran, Adam Wang, and Demetri Terzopoulos. Our paper was accepted to ISBI 2021 in the full-paper track and was presented at the conference in April. The extension of our paper with improved results was published in the MELBA Journal as well. This article will cover a review of the methods, results, and a short code review. The code is available here.

Overview:

MultiMix performs joint semi-supervised classification and segmentation by employing a confidence-based augmentation strategy and a novel saliency bridge module which provides explainability for the joint tasks. Deep learning-based models, when fully-supervised can be efficient in performing complex image analysis tasks, but this performance relies heavily upon the availability of large labeled datasets. Especially in the medical imaging domain, labels are expensive, time-consuming, and prone to observer variations. As a result, semi-supervised learning, which allows for learning from limited quantities of labeled data has been…