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• W4311447777 abstract "Abstract Objective . In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT. Approach . Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases. Main results . A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement ( p < 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement. Significance . The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening." @default.
• W4311447777 created "2022-12-26" @default.
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• W4311447777 date "2023-02-10" @default.
• W4311447777 modified "2023-10-18" @default.
• W4311447777 title "Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field" @default.
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• W4311447777 doi "https://doi.org/10.1088/1361-6560/acaba7" @default.
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• W4311447777 hasPublicationYear "2023" @default.
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