Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning

TL;DR

This paper introduces MLCAK, a method using Vision Transformers and multi-task learning to improve low-resolution chest X-ray diagnosis by transferring knowledge from high-resolution images, enhancing accuracy and explainability.

Contribution

The paper proposes a novel multi-task learning framework with self-attention mechanisms to effectively transfer diagnostic knowledge from high- to low-resolution CXRs.

Findings

Significant improvement in low-resolution CXR diagnosis accuracy

Effective knowledge transfer from high- to low-resolution images

Enhanced model explainability with local pathological features

Abstract

This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions, a common limitation in resource-constrained healthcare settings. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. However, when images are downsized for processing in Computer-Aided Diagnosis (CAD) systems, vital spatial details and receptive fields are lost, hampering diagnosis accuracy. To address this, this paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method. This approach leverages the self-attention mechanism of Vision Transformers (ViT) to transfer critical diagnostic knowledge from high-resolution images to enhance the diagnostic efficacy of low-resolution CXRs. MLCAK incorporates local pathological findings to boost model explainability, enabling more accurate global predictions in a multi-task framework tailored for low-resolution CXR analysis. Our research, utilizing the Vindr CXR dataset, shows a considerable enhancement in the ability to diagnose diseases from low-resolution images (e.g. 28 x 28), suggesting a critical transition from the traditional reliance on high-resolution imaging (e.g. 224 x 224).