Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them. Prior studies show that pre-trained V&L models can significantly improve the model performance for downstream tasks such as Visual Question Answering (VQA). However, V&L models are less effective when applied in the medical domain (e.g., on X-ray images and clinical notes) due to the domain gap. In this paper, we investigate the challenges of applying pre-trained V&L models in medical applications. In particular, we identify that the visual representation in general V&L models is not suitable for processing medical data. To overcome this limitation, we propose BERTHop, a transformer-based model based on PixelHop++ and VisualBERT, for better capturing the associations between the two modalities. Experiments on the OpenI dataset, a commonly used thoracic disease diagnosis benchmark, show that BERTHop achieves an average Area Under the Curve (AUC) of 98.12% which is 1.62% higher than state-of-the-art (SOTA) while it is trained on a 9 times smaller dataset.
@article{arxiv.2108.04938,
title = {BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis},
author = {Masoud Monajatipoor and Mozhdeh Rouhsedaghat and Liunian Harold Li and Aichi Chien and C. -C. Jay Kuo and Fabien Scalzo and Kai-Wei Chang},
journal= {arXiv preprint arXiv:2108.04938},
year = {2021}
}