A turning point few-shot learning for COVID-19 diagnosis

Abstract

The current landscape of medical diagnostics grapples with a critical challenge posed by the limitations of existing meta-learning techniques in interpreting complex representations from limited labeled data, particularly evident in COVID-19 datasets. In response to this gap, our paper introduces a groundbreaking Turning Point (TP) methodology designed to enhance the interpretability of machine learning diagnostics, specifically addressing the shortcomings highlighted in conventional meta-learning approaches. Our Turning Point-based Few-Shot Learning (TPFSL) model goes beyond traditional FSL methods by embracing structured knowledge representation, departing from unstructured metric spaces. We found that our TPFSL model outperformed the state-of-the-art models by an average of 4.50% in 1-shot learning and 4.43% in 5-shot learning after conducting extensive benchmarking on the COVCT, SARSCOV2, and SIRM datasets. Across all datasets studied, TPFSL outperforms the ProtoNet benchmark in 1-shot classification by 12.966% and in 5-shot classification by 11.033%. The importance of TP density, the structure of the network, and placement in improving model performance have been shown by a thorough set of ablation investigations; with its revolutionary TPFSL model, which tackles the shortcomings of current meta-learning methods head-on, COVID-19 diagnostic procedures can be made more accurate and valid in clinical situations.