遺伝的アルゴリズムとSHAP値を用いた説明可能なCNNによるアルツハイマー病予測
Mohammad Zahedipour1, Mohammad Saniee Abadeh1,2, Shakila Shojaei1,3
1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
PloS one
|January 16, 2026
まとめ
本研究では、遺伝的アルゴリズムとSHAPを組み合わせた新しい説明可能なAI技術であるGASHAPを紹介し、MRIスキャンを使用したアルツハイマー病診断のための3D-CNNモデルの透明性を向上させる。
科学分野:
- 人工知能
- 医療画像解析
- 神経科学
背景:
- 3D-CNNのようなディープラーニングモデルは画像分類に優れていますが、透明性がありません。
- ブラックボックスモデルの解釈は、特にアルツハイマー病のような疾患の診断において、ヘルスケアでは困難です。
- 説明可能なAI(XAI)技術は、モデルの解釈可能性を向上させることを目的としています。
主な方法:
- MRI脳スキャンでアルツハイマー病を分類するための3D-CNNを実装しました。
- GAとSHAPを組み合わせたGASHAP技術を適用して、重要な脳領域を特定しました。
- アルツハイマー病診断の重要な領域を強調する決定的な脳マスクを生成しました。
結論:
- GASHAPは、医療画像における3D-CNNの解釈可能性を向上させます。
- この技術は、アルツハイマー病診断に重要な脳領域の特定に役立ちます。
- このアプローチは、神経変性疾患の検出におけるAIの臨床的有用性を高めます。
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