Modality-bridge Transfer Learning for Medical Image Classification

TL;DR

This paper introduces a modality-bridge transfer learning method for medical image classification that effectively addresses the challenge of limited labeled data by using an intermediate bridge database within the same imaging modality.

Contribution

It proposes a novel transfer learning framework employing a bridge database to better adapt from natural images to medical images within the same modality.

Findings

Achieves high classification accuracy with few labeled target images.

Outperforms existing transfer learning methods in medical image classification.

Effectively mitigates domain differences between source and target data.

Abstract

This paper presents a new approach of transfer learning-based medical image classification to mitigate insufficient labeled data problem in medical domain. Instead of direct transfer learning from source to small number of labeled target data, we propose a modality-bridge transfer learning which employs the bridge database in the same medical imaging acquisition modality as target database. By learning the projection function from source to bridge and from bridge to target, the domain difference between source (e.g., natural images) and target (e.g., X-ray images) can be mitigated. Experimental results show that the proposed method can achieve a high classification performance even for a small number of labeled target medical images, compared to various transfer learning approaches.