BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis

• Published in: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) pp. 3327 - 3336
• Main Authors: Monajatipoor, Masoud, Rouhsedaghat, Mozhdeh, Li, Liunian Harold, Chien, Aichi, Jay Kuo, C.-C., Scalzo, Fabien, Chang, Kai-Wei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2021
• Subjects: Computer vision; Conferences; Data models; Knowledge discovery; Medical diagnosis; Transformers; Visualization
• ISSN: 2473-9944
• DOI: 10.1109/ICCVW54120.2021.00372

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 Openl 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 9x smaller dataset.