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29 May 2024 Sample-efficient framework for breast lesion detection in digital breast tomosynthesis: preliminary analysis on its generalizability
Md Belayat Hossain, Robert M. Nishikawa, Juhun Lee
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Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131740A (2024) https://doi.org/10.1117/12.3027021
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States
Abstract
The purpose of this study was to test the generalizability of our sample-efficient lesion detection framework for biopsy-proven breast lesions detection on digital breast Tomosynthesis (DBT). We developed a sample-efficient breast lesion detection framework using a set of limited biopsied DBT lesions. Instead of using large in-house lesion dataset that only a few can access, we utilized non-biopsied false positive findings to augment the limited training set. We applied our framework on open-source single and multi-stage Convolutional Neural Network based object detectors to show the generalizability of our framework. Then, we combined different detector models using ensemble approach to further improve the detection performance. Using a challenge validation set, we achieved detection performance (a mean sensitivity of 0.84 FPs per DBT volume and sensitivity of 0.80 at 2 false positives per image) close to one of top-ranking algorithms in the DBT lesion detection challenge which augmented the training set with a large in-house mammogram dataset.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Md Belayat Hossain, Robert M. Nishikawa, and Juhun Lee "Sample-efficient framework for breast lesion detection in digital breast tomosynthesis: preliminary analysis on its generalizability", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740A (29 May 2024); https://doi.org/10.1117/12.3027021
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KEYWORDS
Digital breast tomosynthesis
Object detection
Breast
Deep learning
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