Toward New-Generation AI-Based CAD System: Development of Interpretable Deep Learning-Based CAD System for Breast Cancer Diagnosis Using Mammogram

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K08012
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: Sendai National College of Technology
• Principal Investigator: Zhang Xiaoyong 仙台高等専門学校, 総合工学科, 准教授 (90722752)
• Co-Investigator(Kenkyū-buntansha): 費 仙鳳 東北文化学園大学, 工学部, 准教授 (20620470)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: マンモグラフィー / 計算機支援診断 / 深層学習 / 説明可能なAI / 乳がん

Outline of Final Research Achievements

The purpose of this study was to develop an explainable AI-based mammographic computer-aided diagnosis (CAD) system for breast cancer detection. We mainly focus on investigating the insight mechanism of AI models in reading a medical image and use it to help clinicians to understand the decision-making of AI models.
A digital mammogram dataset consisting of 30,000 cases with train labels were collected for training AI system. Several deep learning (DL)-based methods were developed for mammogram classification and mass detection in mammograms. Visualization techniques were utilized to generate visual explanations of diagnosis results. In addition, a domain shift issue related to train data was also investigated in this study.

Free Research Field

医用画像処理

Academic Significance and Societal Importance of the Research Achievements

本研究では、AIのブラックボックス性を解消するため、AI内部の可視化などの技術を用いて、説明可能なAI診断システムの開発を目的とする。説明可能なAI診断システムの開発は臨床面でも非常に重要な意義をもつ。本研究では、画像解剖学的知見に基づく解析により、診断根拠の解釈を可能とすることで、正確な診断だけでなく信頼性の高い医療AIシステムの実用が可能であることを実証した。