Advancing Radiograph Representation Learning with Masked Record Modeling

• URL: http://arxiv.org/abs/2301.13155v1
• Date: Mon, 30 Jan 2023 18:33:32 GMT
• Title: Advancing Radiograph Representation Learning with Masked Record Modeling
• Authors: Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, Yizhou Yu
• Abstract summary: We formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM) MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning.
• Score: 52.04899592688968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins.

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