Unsupervised, Semi-supervised, Weakly-supervised Learning in Biomedical Image Analysis

DOI

Bibliographic Information

Other Title
  • 教師なし・半教師あり・弱教師あり学習の最先端とバイオ医療画像応用

Description

<p>Supervised learning such as deep learning has applied to various tasks and achieved high accuracy in biomedical image analysis. However, it is required to prepare sufficient labeled data to learn discriminative features for robust recognition. It often requires significant effort by biomedical experts to annotate images for various objects, imaging modality and types of disease. In this paper, I introduce current works about unsupervised, semi-supervised, and weakly-supervised learning, which enables to reduce the annotation costs. I then introduce our researches on these learning problems.</p>

Journal

  • Medical Imaging Technology

    Medical Imaging Technology 39 (4), 135-141, 2021-09-25

    The Japanese Society of Medical Imaging Technology

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Details 詳細情報について

  • CRID
    1390008597681058432
  • NII Article ID
    130008116934
  • DOI
    10.11409/mit.39.135
  • ISSN
    21853193
    0288450X
  • Text Lang
    ja
  • Article Type
    journal article
  • Data Source
    • JaLC
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Disallowed

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