C3MT:半教師あり医用画像セグメンテーションのための信頼度校正型対照平均教師
Xianmin Wang1, Mingfeng Lin1, Jing Li2
1Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511442, China.
まとめ
C3MT(Confidence-Calibrated Contrastive Mean Teacher)は、新しい半教師あり学習フレームワークであり、医用画像セグメンテーションを改善します。C3MTは、特にラベル付きデータが限られている場合に、特徴表現とセグメンテーションの質を向上させます。
背景:
- ラベル付きデータが限られているため、半教師あり学習は医用画像セグメンテーションに不可欠です。既存の方法は、特徴表現、サブネットワーク間の不一致、ノイズの多い疑似ラベルに苦労しています。
結論:
- C3MTフレームワークは、既存の方法の主な課題に対処する、半教師あり医用画像セグメンテーションのための堅牢なソリューションを提供します。提案された戦略は、最小限のラベル付きデータであっても、セグメンテーションの精度と信頼性を効果的に向上させます。
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