医科学専攻 保健学専攻

  • Doctoral Courses 
    博士課程

Clinical Imaging画像診断学

  • 画像診断
  • 診断支援システム
  • イメージング
  • 数値流体力学
  • CT
  • MRI
  • 超音波
  • 機械学習
  • 人工知能
  • 深層学習

STAFF

Professor

  • Ueda, TakuyaProfessor. 植田 琢也 教授

Other Faculty / Staff

  • Kobayashi, Tomoya
    Assistant Prof. 小林 智哉 助教

CONTACT

TEL:+81-22-717-7481
E-MAIL:tohokuclinicalimaging*cimage.med.tohoku.ac.jp
(「*」を「@」に変換してください)

OUTLINE

The Department of Diagnostic Imaging aims to create technologies that really can be applied in clinical practice, and combines medical and mathematical methods.The main research topics include AI-based diagnostic imaging research on breast and neurological diseases,and research on improving the accuracy of AI through pre-image processing of image data.A variety of people, including doctors specializing in medicine, radiological technologists specializing in image processing, and data scientists specializing in applied mathematics and AI, participate the project, which promotes a multidisciplinary approach to research.
Moreover,our department collaborates with the Tohoku University Advanced Graduate Program for Future Medicine and Health Care, where Professor Ueda serves as a facilitator. The program aims to nurture human resources who are able to find medical problems and proposes the solutions by themselves by discussing and facilitating with their professors, and to develop the skills, knowledge, and solutions necessary to achieve their goals. We are training students to become professionals of future medicine using data science and new technologies.
We accepts a variety of people interested in medical AI research, including graduate students (Ph.D. and M.D.) from the School of Medicine and Department of Health Sciences as well as medical undergraduate students, research students, corporate AI engineers, and interns from overseas, all of whom work together in research and study with an attitude of “learning together”.

我々の分野では、臨床医療の現場に活かせる技術の創出を目標として、医療分野に数理学的な手法を組み合わせた研究を行っています。主な研究テーマは、人工知能(AI)を用いた乳腺疾患などの画像診断の研究、画像データの前処理が人工知能(AI)の精度に及ぼす影響の研究、心不全の大規模データ解析などがあります。医療を専門とする医師、画像処理を専門とする放射線技師、人工知能(AI)の専門家であるデータサイエンティストなど、さまざまな人員がプロジェクトを構成しており、多方向から研究テーマにアプローチしています。
また東北大学医療AI人材育成拠点のコーディネーターとして、データサイエンス・新しいテクノロジーを駆使した未来型医療人材の育成を行っています。研究室では、議論を通じて学生自らが自発的に課題を見つけ、目標達成に必要なスキル・知識と解決策を考えられるような人材育成を心がけています。保健学科・医学科の大学院生(博士/修士)のほか、医学部学生・社会人研究生・企業AIエンジニア・海外からのインターン生など、医療AIに興味のある様々な人材と、「共に学ぶ」という姿勢で研究や勉強を進めていきたいと思っています。

  • Various AI diagnostic imaging studies for breast cancer
    乳癌のトモシンセシス画像を用いたAI画像診断研究を様々な方向から掘り下げています。

  • Machine learning model that predicts the prior probability of lesions from the difference in tomosynthesis imaging conditions
    トモシンセシス撮影条件の差から病変の事前確率を予測する機械学習モデル

  • Improvement of deep learning accuracy by preprocessing of image data
    画像データの前処理による深層学習精度の向上

  • Ueda Lab
    植田研究室

ARTICLE

Ueda T. CT-guided mapping in the removal of an impalpable, radiopaque foreign body in subcutaneous tissue: a case report. Journal of Wound Care.29:424-426. 2020
URL:http://www.magonlinelibrary.com/doi/pdf/10.12968/jowc.2020.29.7.424

Ueda T. Computational Fluid Dynamics Modeling in Aortic Diseases. Cardiovasc Imaging Asia. 2:58-64. 2018

Ueda T. A geometrical characteristics study in patient-specific FSI analysis of blood flow in the thoracic aorta Advances in computational fluid structure interaction and flow simulation: New methods and challenging computations. 29: 379-386, 2016
URL:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992382819&doi=10.1007%2f978-3-319-40827-9_29&partnerID=40&md5=cf5ef02213019129048bc08821061404

Ueda T. Detection of Broken Sutures and Metal-Ring Fractures in AneuRx Stent-Grafts by Using Three-dimensional CT Angiography after Endovascular Abdominal Aortic Aneurysm Repair: Association with Late Endoleak Development and Device Migration. Radiology. 272: 275-83, 2014
URL:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903704407&doi=10.1148%2fradiol.14130920&partnerID=40&md5=b677ad84d55ebfb54c6deabaf6b1e238

Ueda T. A pictorial review of acute aortic syndrome: discriminating and overlapping features as revealed by ECG-gated multidetector-row CT angiography. Insights Imaging 3: 561-571; 2012
URL:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874199200&doi=10.1007%2fs13244-012-0195-7&partnerID=40&md5=f0aa3a2ae1b1e0eddbcc794caeca3df4

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