Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment

• URL: http://arxiv.org/abs/2109.01949v1
• Date: Sat, 4 Sep 2021 22:58:35 GMT
• Title: Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
• Authors: Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber, Sargur Srihari, Yifan Peng, Mingchen Gao
• Abstract summary: This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching.
• Score: 9.265044250068554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses. The region-word matching is calculated using the attention mechanism without direct supervision about their mapping. The pre-trained multi-modal representation learning paves the way for downstream tasks concerning image and/or text encoding. We demonstrate the representation learning quality by cross-modality retrievals and multi-label classifications on two datasets: OpenI-IU and MIMIC-CXR

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