説明可能なメラノーマ自動診断のための半教師あり学習およびマルチタスク学習を用いた診断指標の予測

• 村林 誠也

書誌事項

タイトル別名

• Towards Explainable Melanoma Diagnosis: Prediction of Clinical Indicators Using Semi-supervised Learning

抄録

type:Article

In this paper, we propose an effective method for predicting explainable melanoma indicators defined by a 7-point checklist in a situation where only a limited number of labeled data are available. Our proposal effectively utilizes virtual adversarial training as a semi-supervised learning framework with multi-task learning. This approach gives favorable performance for only a very limited number of expensive labeled data. The proposed method improves the final accuracy of melanoma diagnosis calculated based on these predicted indices by 7.5% (making it equivalent to expert dermatologists), based on 9,124 unlabeled images with diagnosis information added to the 226 base labeled training images.

収録刊行物

• 法政大学大学院紀要. 理工学・工学研究科編

  法政大学大学院紀要. 理工学・工学研究科編 61 1-2, 2020-03-24

  法政大学大学院理工学研究科

キーワード

• melanoma
• computer-aided diagnosis (CAD)
• deep learning
• semi-supervised learning
• multi-task learning
• 7-point checklist

詳細情報

• CRID: 1390009224831366528
• NII論文ID: 120006896995
• NII書誌ID: AA12677220
• ISSN: 21879923
• DOI: 10.15002/00022907
• Web Site: http://hdl.handle.net/10114/00022907
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • IRDB
  • CiNii Articles