WEKO3
アイテム
From Divergent LLM Predictions to Reliable Lung Cancer Staging with Ensemble Fusion: CYUT at the NTCIR-18 RadNLP Main Task
https://doi.org/10.20736/0002002064
https://doi.org/10.20736/00020020643ea7f230-a4d2-4a5a-931d-ddd9f95016bc
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
04-NTCIR18-RADNLP-LauT.pdf (1.5 MB)
|
|
| 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 | From Divergent LLM Predictions to Reliable Lung Cancer Staging with Ensemble Fusion: CYUT at the NTCIR-18 RadNLP Main Task | |||||||||
| 言語 | en | |||||||||
| 作成者 |
Tsz-Yeung Lau
× Tsz-Yeung Lau
× Shih-Hung Wu
|
|||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | This study investigates the application of Large Language Models (LLMs) for automated lung cancer staging based on radiology reports, as part of the CYUT team’s participation in the NTCIR-18 RadNLP Main Task. Through data analysis, we observed a moderate correlation among the T, N, and M staging classes. Experimental results indicated that jointly prompting LLMs to predict all three classes simultaneously yields improved performance. Additionally, standardizing measurement units to millimeters, rather than centimeters, proved to be a more effective strategy. Based on these findings, we refined our prompting methodology and applied it to both LLMs and reasoning-augmented models, including OpenAI’s O-series and DeepSeek-R1. These reasoning-models, enhanced through post-training with Chain-of-Thought (CoT) reasoning, demonstrated superior staging accuracy. As LLMs are generative models, their outputs may vary across different runs, introducing inconsistency in predictions. To mitigate this variability, we adopted an ensemble learning strategy aimed at consolidating divergent LLM outputs into a more stable and reliable lung cancer staging system. Experimental results demonstrate that ensemble methods consistently outperform individual models, enhancing both the robustness and reliability of staging from radiology reports. Our approach achieved second place in the NTCIR-18 RadNLP Main Task (English), underscoring the effectiveness of LLM-based ensemble techniques for TNM classification. The implementation is available at github: anson70242/NTCIR-18-RadNLP-CYUT. | |||||||||
| 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 | 10.20736/0002002064 | |||||||||
| 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 | |||||||||
