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Updated: Jan 27, 2026

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医療画像セグメンテーションにおける微分可能ニューラルアーキテクチャサーチ:系統的レビューとフィールド監査
Emil Benedykciuk1, Marcin Denkowski1, Grzegorz M Wójcik1
1Institute of Computer Science and Mathematics, Maria Curie Sklodowska University, Akademicka 9, Lublin, 20-033, Poland.
まとめ
微分可能ニューラルアーキテクチャサーチ(DNAS)は医療画像セグメンテーションに有望であるが、課題に直面している。系統的レビューでは、外部検証、コードリリース、検索安定性への焦点が限定的であり、臨床利用を妨げていることが判明した。
背景:
- 医療画像セグメンテーションは臨床アプリケーションに不可欠ですが、体積データやドメインシフトなどの特有の課題に直面しています。
- 従来のニューラルアーキテクチャサーチ(NAS)は、3D医療画像セグメンテーションにとって計算コストが高いです。
- 微分可能NAS(DNAS)は、共有スーパーネット内で勾配ベースの最適化を使用することにより、より実行可能なアプローチを提供します。
結論:
- DNASは自動医療画像セグメンテーションモデル設計に有望ですが、現在の実践は検証、再現性、および報告において重大なギャップを示しています。
- DNAS由来モデルの臨床展開可能性を高めるためには、標準化された報告と方法論が不可欠です。
- 提案されたNAS報告カードは、医療セグメンテーション研究における透明性と比較可能性を向上させることを目指しています。
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