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ORAD at NTCIR-18 RadNLP 2024 Shared Task
https://doi.org/10.20736/0002002071
https://doi.org/10.20736/000200207149fad783-0115-421f-8011-a8f5bbabf5b6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
11-NTCIR18-RADNLP-HidakaK.pdf (3.7 MB)
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| アイテムタイプ | デフォルトアイテムタイプ（フル）(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-06-06 | |||||||
| タイトル | ||||||||
| タイトル | ORAD at NTCIR-18 RadNLP 2024 Shared Task | |||||||
| 言語 | en | |||||||
| 作成者 |
Keisuke Hidaka
× Keisuke Hidaka
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| 内容記述 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Here, we report our approach to the NTCIR-18 RadNLP2024 Shared Task (Japanese Track, Main Task). In this study, we developed a system to determine the TNM classification from lung cancer using Japanese radiology reports. Specifically, we provided Google DeepMind’s Gemini 2.0 Flash Experimental (gemini-2.0-flash-exp) with a prompt that combines Chain-of-Thought (CoT) and Many-Shot In-Context Learning (ICL), enabling automatic prediction of the T, N, and M factors for each case. Besides accuracy, interpretability is crucial in the medical domain; thus, having the model output the rationale for its TNM classification ensures a degree of transparency. Moreover, by including numerous examples of CoT-based reasoning—written by a radiologist with 5 years of dedicated experience in diagnostic radiology—to explain how the TNM classification is derived, we achieved improved inference accuracy. Furthermore, to address privacy concerns and the need for local inference without network connectivity in clinical settings, we performed Supervised Fine-Tuning (SFT) using Gemma2-9b-it, a comparatively lightweight open-source model. By providing the model with CoT-based reasoning steps leading to TNM classification as training data, we observed improved inference accuracy. These findings demonstrate that additional data and prompt strategies to support large language model (LLM)-based inference can be highly effective in automating TNM classification while also indicating the feasibility of realizing interpretability in LLM-based medical applications. | |||||||
| 言語 | en | |||||||
| 出版者 | ||||||||
| 出版者 | NII Institutional Repository | |||||||
| 言語 | en | |||||||
| 日付 | ||||||||
| 日付 | 2025-06-06 | |||||||
| 日付タイプ | Issued | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||
| 資源タイプ | conference paper | |||||||
| ID登録 | ||||||||
| ID登録 | 10.20736/0002002071 | |||||||
| ID登録タイプ | JaLC | |||||||
| 関連情報 | ||||||||
| 関連タイプ | isReferencedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://research.nii.ac.jp/ntcir/ntcir-18/index.html | |||||||
| 言語 | en | |||||||
| 関連名称 | NTCIR-18 Conference | |||||||
| 開始ページ | ||||||||
| 開始ページ | none | |||||||
| 会議記述 | ||||||||
| 会議名 | NTCIR-18 Conference | |||||||
| 言語 | en | |||||||
| 回次 | 18 | |||||||
| 主催機関 | National Institute of Informatics | |||||||
| 言語 | en | |||||||
| 開始年 | 2025 | |||||||
| 開始月 | 6 | |||||||
| 開始日 | 10 | |||||||
| 終了年 | 2025 | |||||||
| 終了月 | 6 | |||||||
| 終了日 | 13 | |||||||
| 開催期間 | June 10-13, 2025 | |||||||
| 言語 | en | |||||||
| 開催会場 | National Institute of Informatics | |||||||
| 言語 | en | |||||||
| 開催国 | JPN | |||||||
