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Proceedings Paper

Investigating the potential of a gist-sensitive computer-aided detection tool
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Paper Abstract

This study explored the possibility of using the gist signal (radiologists’ first impression about a case) for improving the performance of two recently developed deep learning-based breast cancer detection tools. We investigated whether by combining the cancer class probability from the networks with the gist signal, higher performance in identifying malignant cases can be achieved. In total, we recruited 53 radiologists, who provided an abnormality score on a scale from 0 to 100 to unilateral mammograms following a 500-millisecond presentation of the image. Twenty cancer cases, 40 benign cases, and 20 normal were included. Two state-ofthe-art deep learning-based tools (M1 and M2) for breast cancer detection were adopted. The abnormality scores from the networks and the gist responses for each observer were fed into a support vector machine (SVM). The SVM was personalized for each radiologist and its performance was evaluated using leave-one-out cross-validation. We also considered the average reader; whose gist responses were the mean abnormality scores given by all 53 readers to each image. The mean and range of AUCs in the gist experiment were 0.643 and 0.492-0.794, respectively. The AUC values for M1 and M2 were 0.789 (0.632-0.892) and 0.814 (0.673-0.897), respectively. For the average reader, the AUC for gist, gist+M1, and gist+M2 were 0.760 (0.617-0.862), 0.847 (0.754-0.928), 0.897 (0.789-0.946). For 45 readers, the performance of at least one of the models improved after aggregating its output with the gist signal. The results showed that the gist signal has the potential to improve the performance of adopted deep learning-based tools.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160J (16 March 2020); doi: 10.1117/12.2549921
Show Author Affiliations
Ziba Gandomkar, Univ. of Sydney (Australia)
Ernest U. Ekpo, Univ. of Sydney (Australia)
Sarah J. Lewis, Univ. of Sydney (Australia)
Moayyad E. Suleiman, Univ. of Sydney (Australia)
Kriscia Tapia, Univ. of Sydney (Australia)
Tong Li, Univ. of Sydney (Australia)
Seyedamir Tavakoli Taba, Univ. of Sydney (Australia)
Phuong Dung Trieu, Univ. of Sydney (Australia)
Patrick C. Brennan, Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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