Efficient Soft-Constrained Clustering for Group-Based Labeling

  • Ryoma BiseEmail author
  • Kentaro Abe
  • Hideaki Hayashi
  • Kiyohito Tanaka
  • Seiichi Uchida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)


We propose a soft-constrained clustering method for group-based labeling of medical images. Since the idea of group-based labeling is to attach the label to a group of samples at once, we need to have groups (i.e., clusters) with high purity. The proposed method is formulated to achieve high purity even for difficult clustering tasks such as medical image clustering, where image samples of the same class are often very distant in their feature space. In fact, those images degrade the performance of conventional constrained clustering methods. Experiments with an endoscopy image dataset demonstrated that our method outperformed various state-of-the-art methods.



This work was supported by JSPS KAKENHI Grant Number JP19K22895 and AMED Grant Number JP18lk1010028.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ryoma Bise
    • 1
    Email author
  • Kentaro Abe
    • 1
  • Hideaki Hayashi
    • 1
  • Kiyohito Tanaka
    • 2
  • Seiichi Uchida
    • 1
  1. 1.Kyushu UniversityFukuoka CityJapan
  2. 2.Kyoto Second Red Cross HospitalKyotoJapan

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