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17 March 2020 A hypersensitive breast cancer detector (Conference Presentation)
Stefano Pedemonte
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Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131402 (2020) https://doi.org/10.1117/12.2549847
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States
Abstract
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity. Building upon work from the Hourglass architecture, we train a model that produces segmentation- like images with high spatial resolution, with the aim of producing 2D Gaussian blobs centered on ground-truth boxes. We replace the pixel-wise L2 norm with a weak-supervision loss designed to achieve high sensitivity, asymmetrically penalizing false positives and false negatives while softening the noise of the loose bounding boxes by permitting a tolerance in misaligned predictions. The resulting system achieves a sensitivity for malignant findings of 0.99 with only 4.8 false positive markers per image. When utilized in a CADe system, this model could enable a novel workflow where radiologists can focus their attention with trust on only the locations proposed by the model, expediting the interpretation process and bringing attention to potential findings that could otherwise have been missed. Due to its nearly perfect sensitivity, the proposed detector can also be used as a high-performance proposal generator in two-stage detection systems.
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Stefano Pedemonte "A hypersensitive breast cancer detector (Conference Presentation)", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131402 (17 March 2020); https://doi.org/10.1117/12.2549847
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KEYWORDS
Breast cancer
Image segmentation
Sensors
Computer aided diagnosis and therapy
Mammography
Breast
Digital mammography
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