Missed cancer and visual search of mammograms: What feature-based machine-learning can tell us that deep-convolution learning cannot

Suneeta Mall, Elizabeth Krupinski, Claudia Mello-Thoms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Significant amount of effort has been invested in improving the quality of breast imaging modalities (for example, mammography) to increase the accuracy of breast cancer detection. Despite that, about 4-34% of cancers are still missed during mammographic examination of cancer of the breast. This indicates the need to explore a) The features of the lesions that are missed, and b) Whether the features of missed cancers contribute to why some cancers are not 'looked at' (search error) whereas others are 'looked at' but still not reported. In this visual search study, we perform feature analysis of all lesions that were missed by at least one participating radiologist. We focus on features extracted by means of Grey Level Co-occurrence Matrix properties, textural properties using Gabor filters, statistical information extraction using 2nd and higher-order (3rd and 4th) spectral analysis and also spatialoral attributes of radiologists' visual search behaviour. We perform Analysis of Variance (ANOVA) on these features to explore the differences in features for cancers that were missed due to a) search, b) perception and c) decision making errors. Using these features, we trained Support Vector Machine, Gradient Boosting and stochastic gradient decent classifiers to determine the type of missed cancer (search, perception and decision making). We compared these feature-based models with a model trained using deep convolution neural network that learns features by itself. We determined whether deep learning or traditional machine learning performs best in this task.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsRobert M. Nishikawa, Frank W. Samuelson
PublisherSPIE
ISBN (Electronic)9781510625518
DOIs
StatePublished - Jan 1 2019
Externally publishedYes
EventMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10952
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CitySan Diego
Period2/20/192/21/19

Keywords

  • Breast Cancer
  • Deep Learning
  • Eye tracking
  • Machine Learning
  • Missed Cancer
  • Visual Search

ASJC Scopus subject areas

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

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  • Cite this

    Mall, S., Krupinski, E., & Mello-Thoms, C. (2019). Missed cancer and visual search of mammograms: What feature-based machine-learning can tell us that deep-convolution learning cannot. In R. M. Nishikawa, & F. W. Samuelson (Eds.), Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment [1095216] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10952). SPIE. https://doi.org/10.1117/12.2512539