4.6 Article

Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset

Journal

JOURNAL OF BIOMEDICAL INFORMATICS
Volume 113, Issue -, Pages -

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2020.103656

Keywords

Mammography; Computer assisted diagnosis; Deep learning; Segmentation

Funding

  1. DARPA [FA86501827865, FA86501827882]
  2. NIH [U54EB020405, U01-CA242879]
  3. NSF [CCF1763315, CCF1563078, 1937301]
  4. ONR [N000141712266]
  5. National Cancer Institute
  6. Moore Foundation
  7. NXP
  8. Xilinx
  9. LETI-CEA
  10. Intel
  11. IBM
  12. Microsoft
  13. NEC
  14. Toshiba
  15. TSMC
  16. ARM
  17. Hitachi
  18. BASF
  19. Accenture
  20. Ericsson
  21. Qualcomm
  22. Analog Devices
  23. Okawa Foundation
  24. American Family Insurance
  25. Google Cloud
  26. Swiss Re
  27. Intelligence Community Postdoctoral Fellowship
  28. Stanford Human-Centered Artificial Intelligence Seed Grants Program
  29. Teradat
  30. Facebook
  31. Google
  32. Ant Financial
  33. VMWare
  34. Infosys

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This study compared machine learning methods for classifying mass lesions on mammography images and found that a common segmentation-free CNN model substantially outperforms other methods. This indicates that representation learning techniques are advantageous for mammogram analysis.
Purpose: To compare machine learning methods for classifying mass lesions on mammography images that use predefined image features computed over lesion segmentations to those that leverage segmentation-free representation learning on a standard, public evaluation dataset. Methods: We apply several classification algorithms to the public Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), in which each image contains a mass lesion. Segmentationfree representation learning techniques for classifying lesions as benign or malignant include both a Bag-ofVisual-Words (BoVW) method and a Convolutional Neural Network (CNN). We compare classification performance of these techniques to that obtained using two different segmentation-dependent approaches from the literature that rely on specific combinations of end classifiers (e.g. linear discriminant analysis, neural networks) and predefined features computed over the lesion segmentation (e.g. spiculation measure, morphological characteristics, intensity metrics). Results: We report area under the receiver operating characteristic curve (AZ) values for malignancy classification on CBIS-DDSM for each technique. We find average AZ values of 0.73 for a segmentation-free BoVW method, 0.86 for a segmentation-free CNN method, 0.75 for a segmentation-dependent linear discriminant analysis of Rubber-Band Straightening Transform features, and 0.58 for a hybrid rule-based neural network classification using a small number of hand-designed features. Conclusions: We find that malignancy classification performance on the CBIS-DDSM dataset using segmentationfree BoVW features is comparable to that of the best segmentation-dependent methods we study, but also observe that a common segmentation-free CNN model substantially and significantly outperforms each of these (p < 0.05). These results reinforce recent findings suggesting that representation learning techniques such as BoVW and CNNs are advantageous for mammogram analysis because they do not require lesion segmentation, the quality and specific characteristics of which can vary substantially across datasets. We further observe that segmentation-dependent methods achieve performance levels on CBIS-DDSM inferior to those achieved on the original evaluation datasets reported in the literature. Each of these findings reinforces the need for standardization of datasets, segmentation techniques, and model implementations in performance assessments of automated classifiers for medical imaging.

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