Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

• URL: http://arxiv.org/abs/2105.11333v1
• Date: Mon, 24 May 2021 15:14:09 GMT
• Title: Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training
• Authors: Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Edward Choi
• Abstract summary: We propose Medical Vision Language Learner (MedViLL) which adopts a Transformer-based architecture combined with a novel multimodal attention masking scheme. We empirically demonstrate the superior downstream task performance of MedViLL against various baselines including task-specific architectures.
• Score: 5.119201893752376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives. In this work we explore a broad set of multi-modal representation learning tasks in the medical domain, specifically using radiology images and the unstructured report. We propose Medical Vision Language Learner (MedViLL) which adopts a Transformer-based architecture combined with a novel multimodal attention masking scheme to maximize generalization performance for both vision-language understanding tasks (image-report retrieval, disease classification, medical visual question answering) and vision-language generation task (report generation). By rigorously evaluating the proposed model on four downstream tasks with two chest X-ray image datasets (MIMIC-CXR and Open-I), we empirically demonstrate the superior downstream task performance of MedViLL against various baselines including task-specific architectures.

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