# Modality-bridge Transfer Learning for Medical Image Classification

arXiv: https://arxiv.org/abs/1708.03111 · HTML: https://mlsanity.com/paper/1708.03111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers](https://mlsanity.com/paper/2203.10456/) (2022)
- [Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation](https://mlsanity.com/paper/2402.16005/) (2024)
- [How much data do I need? A case study on medical data](https://mlsanity.com/paper/2311.15331/) (2023)

## Abstract (arXiv)

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.