Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging

TL;DR

This paper introduces ETSEF, a novel ensemble framework combining transfer and self-supervised learning, to improve diagnostic accuracy in medical imaging tasks with limited data, while also providing explainability.

Contribution

ETSEF is the first method to combine transfer learning, self-supervised learning, and ensemble techniques for medical diagnostics under data scarcity.

Findings

ETSEF improves diagnostic accuracy by up to 14.4% over state-of-the-art methods.

ETSEF demonstrates robustness and explainability across five medical imaging tasks.

ETSEF outperforms strong ensemble baselines with limited data samples.

Abstract

Deep learning has significantly advanced automatic medical diagnostics and released the occupation of human resources to reduce clinical pressure, yet the persistent challenge of data scarcity in this area hampers its further improvements and applications. To address this gap, we introduce a novel ensemble framework called `Efficient Transfer and Self-supervised Learning based Ensemble Framework' (ETSEF). ETSEF leverages features from multiple pre-trained deep learning models to efficiently learn powerful representations from a limited number of data samples. To the best of our knowledge, ETSEF is the first strategy that combines two pre-training methodologies (Transfer Learning and Self-supervised Learning) with ensemble learning approaches. Various data enhancement techniques, including data augmentation, feature fusion, feature selection, and decision fusion, have also been deployed to maximise the efficiency and robustness of the ETSEF model. Five independent medical imaging tasks, including endoscopy, breast cancer, monkeypox, brain tumour, and glaucoma detection, were tested to demonstrate ETSEF's effectiveness and robustness. Facing limited sample numbers and challenging medical tasks, ETSEF has proved its effectiveness by improving diagnostics accuracies from 10\% to 13.3\% when compared to strong ensemble baseline models and up to 14.4\% improvements compared with published state-of-the-art methods. Moreover, we emphasise the robustness and trustworthiness of the ETSEF method through various vision-explainable artificial intelligence techniques, including Grad-CAM, SHAP, and t-SNE. Compared to those large-scale deep learning models, ETSEF can be deployed flexibly and maintain superior performance for challenging medical imaging tasks, showing the potential to be applied to more areas that lack training data