Related papers: Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

URL: http://arxiv.org/abs/2010.12316v1
Date: Fri, 23 Oct 2020 11:47:28 GMT
Title: Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Authors: Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov and Thomas Seidl
Abstract summary: Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data.
Score: 2.891413712995642
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in medical image analysis literature. Instead, most practitioners and researchers focus on supervised or transfer learning approaches. The recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations while requiring very few labels. Motivated by these recent successes, we apply MixMatch and FixMatch in an ophthalmological diagnostic setting and investigate how they fare against standard transfer learning. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data. Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged. Our code is available online: https://github.com/Valentyn1997/oct-diagn-semi-supervised