Semi-supervised learning in medical image database

Chun Hung Li, Pong Chi YUEN

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

8 Citations (Scopus)


This paper presents a novel graph-based algorithm for solv- ing the semi-supervised learning problem. The graph-based algorithm makes use of the recent advances in stochastic graph sampling technqiue and a modeling of the labeling consistency in semi-supervised learning. The quality of the algorithm is empirically evaluated on a synthetic clus- tering problem. The semi-supervised clustering is also applied to the problem of symptoms classification in medical image database and shows promising results.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
EditorsDavid Cheung, Graham J. Williams, Qing Li
PublisherSpringer Verlag
Number of pages7
ISBN (Print)3540419101, 9783540419105
Publication statusPublished - 2001
Event5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong
Duration: 16 Apr 200118 Apr 2001

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN (Print)0302-9743


Conference5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
Country/TerritoryHong Kong

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Semi-supervised learning in medical image database'. Together they form a unique fingerprint.

Cite this