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Malignant microcalcification clusters detection using unsupervised deep autoencoders
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Paper Abstract

Detection and localization of microcalcification (MC) clusters are very important in mammography diagnosis. Supervised MC detectors require learning from extracted individual MCs and MC clusters. However, they are limited by number of datasets given that MC images are hard to obtain. In this work, we propose a method to detect malignant microcalcification (MC) clusters using unsupervised, one-class, deep convolutional autoencoder. Specifically, we designed a deep autoencoder model where only patches extracted from normal cases’ mammograms are used during training. We then applied our trained model on patches extracted from testing images. Our training dataset contains 408 normal subjects, including 1961 full-field digital mammography images. Our testing datasets contains 276 subjects. Specifically, 106 of them were patients diagnosed with Ductal Carcinoma In-Situ (DCIS); 70 of them were diagnosed with Invasive Ductal Carcinoma (IDC); the rest 100 are normal cases containing 484 negative screening mammograms. Patches extracted from DCIS and IDC cases (positive patches) contain MC clusters, whereas patches extracted from normal cases (negative patches) don’t. As the model is trained only on negative images that do not contain MCs, it cannot reconstruct MCs well, and thus, the reconstruction error will be larger on positive patches than negative patches. Our detection algorithm’s decision is made based on Max-Squared Error between autoencoder’s input and output patches. To confirm the results were not simply due to blurring, we then compared our designed detector with unsharp mask with Gaussian blur results. The results using the unsupervised autoencoder on testing patches with size 64×64 achieves an AUC result of 0.93. The best performance on testing patches using Gaussian blur with kernel size equal to 11has an overall AUC of 0.82.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502Q (13 March 2019); doi: 10.1117/12.2512829
Show Author Affiliations
Rui Hou, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)
Yinhao Ren, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)
Lars J. Grimm, Duke Univ. School of Medicine (United States)
Maciej A. Mazurowski, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)
Jeffrey R. Marks, Duke Univ. School of Medicine (United States)
Lorraine King, Duke Univ. School of Medicine (United States)
Carlo C. Maley, Arizona State Univ. (United States)
E. Shelley Hwang, Duke Univ. School of Medicine (United States)
Joseph Y. Lo, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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