Development of a Sequential Data Cleansing Technique Based on Machine Learning for Medical Imaging

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K19857
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: Hokkaido University
• Principal Investigator: Togo Ren 北海道大学, 情報科学研究院, 特任助教 (60840395)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 医用画像 / 深層学習 / AI / 機械学習 / データセット

Outline of Final Research Achievements

This study aims to develop a machine learning-based data cleansing technology for stomach X-ray images. In the field of medical image analysis, supervised learning based on large-scale data is increasingly recognized for its effectiveness. However, many of the currently proposed methods focus only on model construction and evaluation, without considering the effort involved in building datasets. To apply diagnostic support technology using machine learning in real-world settings, it is necessary to consider the total performance, including the cost of labeling data. Therefore, this research focuses on the aspect of dataset construction, which is essential for the societal implementation of machine learning, and aims to develop a technology that can efficiently perform data cleansing.

Free Research Field

AI

Academic Significance and Societal Importance of the Research Achievements

本研究では，胃Ｘ線画像を対象とした機械学習に基づくデータクレンジング技術の基礎理論の構築に成功した．医用画像解析分野において，これまで，データセットの構築に係る労力については考慮されてこなかった．本研究では，機械学習モデル構築におけるデータへのラベリングコストを削減可能な理論を構築し，実際に医療データを用いることで有効性を示した．本研究によりデータセット構築に係る労力を削減可能とすることで，あらゆる医用画像に対する診断支援技術としての社会実装の加速に貢献することが期待できる．