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Proceedings Paper

Deep learning model observer for 4-alternative forced choice in digital breast tomosynthesis
Author(s): Seungyeon Choi; Sunghoon Choi; Young-Wook Choi; Hee-Joung Kim
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Paper Abstract

The purpose of this study is to investigate deep learning model observer (DLMO) using classification network for various sizes of mass detection tasks in digital breast tomosynthesis (DBT). Different tube current and sweep angular range acquisition settings were repeated to acquire different DBT images. As a result, a total number of 66 cases of four alternative forced choice (4AFC) reading was undertaken to the human observer and DLMO. The images of spheroidal mass with different sizes of 1.8, 2.3, 3.1, 3.9, 4.7, and 6.3 mm in the target slab of CIRS breast phantom were cropped by 200x200 size for their region of interest (ROI). A percentage of correct responses (𝑃c) was measured at the end of each human observer test and compared by the accuracy of prediction using DLMO. The results indicated that our proposed DLMO showed the averaged training accuracy 93% using 60 testing datasets after 50 epochs of training using 204 input training datasets. Both the accuracy and loss function in training session of categorical cross-entropy reached a plateau after 35 epochs. The sensitivity and specificity of the testing data from DLMO showed 78% and 98%, respectively, which is comparable to the 𝑃c, 0.89, on 4AFC test from the human observer. Although the current number of datasets is too small to apply in clinical trials, the authors considered that our DLMO on the phantom study is useful for initial trial of analysis on DBT images.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113125I (16 March 2020); doi: 10.1117/12.2549504
Show Author Affiliations
Seungyeon Choi, Yonsei Univ. (Korea, Republic of)
Sunghoon Choi, Emory Univ. School of Medicine (United States)
Young-Wook Choi, Korea Electrotechnology Research Institute (Korea, Republic of)
Hee-Joung Kim, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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