Koji Fujimoto


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Boosting Radiology Report Generation by Infusing Comparison Prior
Sanghwan Kim | Farhad Nooralahzadeh | Morteza Rohanian | Koji Fujimoto | Mizuho Nishio | Ryo Sakamoto | Fabio Rinaldi | Michael Krauthammer
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains; these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy can be attributed to a knowledge gap between radiologists and the generation models. While radiologists possess patient-specific prior information, the models solely receive X-ray images at a specific time point. To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports. This extracted comparison prior is then seamlessly integrated into state-of-the-art transformer-based models, enabling them to produce more realistic and comprehensive reports. Our method is evaluated on English report datasets, such as IU X-ray and MIMIC-CXR. The results demonstrate that our approach surpasses baseline models in terms of natural language generation metrics. Notably, our model generates reports that are free from false references to non-existent prior exams, setting it apart from previous models. By addressing this limitation, our approach represents a significant step towards bridging the gap between radiologists and generation models in the domain of medical report generation.


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Progressive Transformer-Based Generation of Radiology Reports
Farhad Nooralahzadeh | Nicolas Perez Gonzalez | Thomas Frauenfelder | Koji Fujimoto | Michael Krauthammer
Findings of the Association for Computational Linguistics: EMNLP 2021

Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps. Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using transformer-based architecture. We follow the transformer-based sequence-to-sequence paradigm at each step. We improve upon the state-of-the-art on two benchmark datasets.


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The Transliteration from Alphabet Queries to Japanese Product Names
Rieko Tsuji | Yoshinori Nemoto | Wimvipa Luangpiensamut | Yuji Abe | Takeshi Kimura | Kanako Komiya | Koji Fujimoto | Yoshiyuki Kotani
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation


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Negation Naive Bayes for Categorization of Product Pages on the Web
Kanako Komiya | Naoto Sato | Koji Fujimoto | Yoshiyuki Kotani
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011