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まとめ

この研究は,ディープラーニングモデルがデジタル病理学でどのように学習するかを理解するための新しい枠組みを導入しています. 私たちは,ニューラルネットワークがトレーニング中に予測可能な内部構造を開発し,病理学のAIに対する解釈性と信頼性を向上させることを発見しました.

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