1. Technical feasibility of automated blur detection in digital mammography using convolutional neural network
- Author
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S. Nowakowska, V. Vescoli, T. Schnitzler, C. Ruppert, K. Borkowski, A. Boss, C. Rossi, B. Wein, and A. Ciritsis
- Subjects
Artificial intelligence ,Deep learning ,Digital mammography ,Image processing (computer-assisted) ,Quality assurance ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Background The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mammography. Methods A retrospective dataset consisting of 152 examinations acquired with mammography machines from three different vendors was utilized. The blurred areas were contoured by expert breast radiologists. Normalized Wiener spectra (nWS) were extracted in a sliding window manner from each mammogram. These spectra served as input for a convolutional neural network (CNN) generating the probability of the spectra originating from a blurred region. The resulting blur probability mask, upon thresholding, facilitated the classification of a mammogram as either blurred or sharp. Ground truth for the test set was defined by the consensus of two radiologists. Results A significant correlation between the view (p
- Published
- 2024
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