深い神経ネットワークを持つ皮膚がんの皮膚科レベルの分類
Andre Esteva1, Brett Kuprel1, Roberto A Novoa2,3
1Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Nature
|January 25, 2017
まとめ
ディープコンヴォルションニューラルネットワーク (CNN) を使用した新しい人工知能モデルは,皮膚科医に匹敵する精度で皮膚がんを分類できます. このAIツールは 大量のデータセットで訓練されていて 携帯機器を通じて 広範な診断使用の可能性を示しています
科学分野:
- 皮膚科
- 人工知能
- 医学画像分析
背景:
- 皮膚がんの診断は,視覚的スクリーニング,皮膚顕微鏡検査,および組織病理学に大きく依存しています.
- 多様な皮膚病変を正確に分類することは,微妙な視覚的変異のために困難です.
- 複雑な画像分類のタスクに期待できる深層のコンボリューションニューラルネットワーク (CNN) が開発されました.
研究 の 目的:
- 臨床画像から直接皮膚病変を端から端まで分類するための単一のCNNを開発し評価する.
- 皮膚がん診断の重要なシナリオにおける 認定皮膚科医に対するAIのパフォーマンスを評価する
- アクセシブルな皮膚がんスクリーニングのためのAI駆動のモバイルデバイスの可能性を探索する.
主な方法:
- 2,032の病気をカバーする 129,450の臨床画像のデータセットで CNNを訓練しました
- ピクセルデータと 病気のラベルを入力した
- CNNのパフォーマンスと21人の皮膚科医を バイオスピーで確認した画像で 比較しました
主要な成果:
- CNNは,ケラチノサイト癌とセボレア性ケラトーシスの分類において,専門の皮膚科医と同等のパフォーマンスを達成しました.
- CNNは悪性メラノマと 良性ネビを区別する際に皮膚科医と同等の精度を示した.
- この研究は,一般的な致命的な皮膚がんを分類するAIの能力を検証しています.
結論:
- CNNを利用した AI システムは皮膚がんの分類において 皮膚科医レベルの能力を獲得できます
- ディープニューラルネットワークに統合されたモバイルデバイスは 皮膚科のケアへのアクセスを大幅に拡大できます
- この技術により 低コストで普遍的な皮膚がん検診が可能になります
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