Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation (2020.findings-emnlp)
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Toru Nishino, Ryota Ozaki, Yohei Momoki, Tomoki Taniguchi, Ryuji Kano, Norihisa Nakano, Yuki Tagawa, Motoki Taniguchi, Tomoko Ohkuma, Keigo Nakamura
| Challenge: | Medical datasets are imbalanced in their finding labels because incidence rates differ among diseases . authors propose a novel reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Approach: | They propose a reinforcement learning method with a reconstructor to improve clinical correctness of generated reports. |
| Outcome: | The proposed method improves clinical correctness of generated reports . it also trains the model on infrequent findings . |
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