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        <title>A Self-training Framework for Automated Medical Report Generation</title>
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        <namePart type="given">Siyuan</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Medical report generation, focusing on automatically generating accurate clinical findings from medical images, is an important medical artificial intelligence task. It reduces the workload of physicians in writing reports. Many of the current methods depend heavily on labeled datasets that include a large amount of image-report pairs, but such datasets labeled by physicians are hard to acquire in clinical practice. To this end, in this paper, we introduce a self-training framework named REMOTE (i.e., Revisiting sElf-training for Medical repOrT gEneration) to exploit the unlabeled medical images and a reference-free evaluation metric MedCLIPScore to augment a small-scale medical report generation dataset for training accurate medical report generation model. Experiments and analysis conducted on the MIMIC-CXR and IU-Xray benchmark datasets demonstrate that, our REMOTE framework, using 1% labeled training data, achieves competitive performance with previous fully-supervised models that are trained on entire training data.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.1024</identifier>
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%0 Conference Proceedings
%T A Self-training Framework for Automated Medical Report Generation
%A Wang, Siyuan
%A Liu, Zheng
%A Peng, Bo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-self-training
%X Medical report generation, focusing on automatically generating accurate clinical findings from medical images, is an important medical artificial intelligence task. It reduces the workload of physicians in writing reports. Many of the current methods depend heavily on labeled datasets that include a large amount of image-report pairs, but such datasets labeled by physicians are hard to acquire in clinical practice. To this end, in this paper, we introduce a self-training framework named REMOTE (i.e., Revisiting sElf-training for Medical repOrT gEneration) to exploit the unlabeled medical images and a reference-free evaluation metric MedCLIPScore to augment a small-scale medical report generation dataset for training accurate medical report generation model. Experiments and analysis conducted on the MIMIC-CXR and IU-Xray benchmark datasets demonstrate that, our REMOTE framework, using 1% labeled training data, achieves competitive performance with previous fully-supervised models that are trained on entire training data.
%R 10.18653/v1/2023.emnlp-main.1024
%U https://aclanthology.org/2023.emnlp-main.1024
%U https://doi.org/10.18653/v1/2023.emnlp-main.1024
%P 16443-16449

Markdown (Informal)

[A Self-training Framework for Automated Medical Report Generation](https://aclanthology.org/2023.emnlp-main.1024) (Wang et al., EMNLP 2023)

• A Self-training Framework for Automated Medical Report Generation (Wang et al., EMNLP 2023)

ACL

• Siyuan Wang, Zheng Liu, and Bo Peng. 2023. A Self-training Framework for Automated Medical Report Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16443–16449, Singapore. Association for Computational Linguistics.