Abstract

Inductive learning refers to machine learning algorithms that learn a model from a set of training data instances. Any test instance is then classified by comparing it to the learned model. When the set of training instances lend themselves well to modeling, the use of a model substantially reduces the computation cost of classification. However, some training data sets are complex, and do not lend themselves well to modeling. Transductive learning refers to machine learning algorithms that classify test instances by comparing them to all of the training instances, without creating an explicit model. This can produce better classification performance, but at a much higher computational cost. Medical images vary greatly across human populations, constituting a data set that does not lend itself well to modeling. Our previous work showed that the wide variations seen across training sets of normal chest radiographs make it difficult to successfully classify test radiographs with an inductive (modeling) approach, and that a transductive approach leads to much better performance in detecting atypical regions. The problem with the transductive approach is its high computational cost. This paper develops and demonstrates a novel semi-transductive framework that can address the unique challenges of atypicality detection in chest radiographs. The proposed framework combines the superior performance of transductive methods with the reduced computational cost of inductive methods. Our results show that the proposed semitransductive approach provides both effective and efficient detection of atypical regions within a set of chest radiographs previously labeled by Mayo Clinic expert thoracic radiologists.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationComputer-Aided Diagnosis
DOIs
StatePublished - Dec 1 2012
EventMedical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 7 2012Feb 9 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8315
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2012: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego, CA
Period2/7/122/9/12

Keywords

  • Anomaly detection
  • Atypicality detection
  • Chest x-rays
  • Computer Aided Diagnosis
  • Inductive learning
  • Machine learning
  • Nearest neighbor
  • Radiology training
  • Semi-transductive learning
  • Transductive learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Alzubaidi, M., Balasubramanian, V., Patel, A., Panchanathan, S., & Black, J. A. (2012). A novel semi-transductive learning framework for efficient atypicality detection in chest radiographs. In Medical Imaging 2012: Computer-Aided Diagnosis [83153A] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8315). https://doi.org/10.1117/12.911145