Conference proceeding
Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, Vol.10952, pp.1095216-1095216-7
Proceedings of SPIE
01/01/2019
DOI: 10.1117/12.2512539
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 [1]. 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 spatial-temporal 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.
Details
- Title: Subtitle
- Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot
- Creators
- Suneeta Malla - Univ Sydney, Sydney, NSW, AustraliaElizabeth Krupinski - Emory UniversityClaudia Mello-Thoms - University of Iowa
- Contributors
- R M Nishikawa (Editor)F W Samuelson (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, Vol.10952, pp.1095216-1095216-7
- Publisher
- Spie-Int Soc Optical Engineering
- Series
- Proceedings of SPIE
- DOI
- 10.1117/12.2512539
- ISSN
- 0277-786X
- eISSN
- 1996-756X
- Number of pages
- 7
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984318699002771
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