Development of a fine tuning model of radiology with deep learning

2021 Fiscal Year Final Research Report

• Project/Area Number: 18K15597
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: Osaka City University
• Principal Investigator: Tsutsumi Shinichi 大阪市立大学, 健康科学イノベーションセンター, 特別研究員 (60647866)
• Project Period (FY): 2018-04-01 – 2022-03-31
• Keywords: 人工知能 / AI / 乳癌 / マンモグラフィ / 説明可能なAI

Outline of Final Research Achievements

We are pleased to present the results of a study in which pathological classification of breast cancer, which is usually determined by invasive pathological methods, could be performed based solely on mammograms. This is the greatest achievement because it was created using a large amount of mammography data in the context of this project funded by this KAKENHI.

Free Research Field

放射線

Academic Significance and Societal Importance of the Research Achievements

今回の研究は、AIの判断根拠を可視化することで、AIを説明に挑戦した。この点ではAIと医師の架け橋となりうるような研究である。また、このモデルはGitHub上でオープンソース(https://github.com/ pathology-mammography)で公開しており、すべての研究者が本モデルを参考することができ、比較やさらなる発展を望むことができる。