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Abstract
Breast cancer is the most common cancer in the world. Mammogram mass detection aids in the early detection of breast cancer and increases patient survival rates. Because the bilateral breasts of the same patient are similar and symmetrical, information fusion of bilateral mammogram images is advantageous in improving the detection rate of masses. However, existing mass detection methods use pixel-level corresponding feature fusion methods, which are sensitive to image registration errors. In this paper, we propose WCA-RCNN, a novel mass detection framework that uses window cross attention to fuse information from bilateral mammogram images. The window cross attention module eliminates pixel-level correspondence and significantly improves mass detection accuracy. In addition, we propose a mass deformation data augmentation method to address the issue of insufficient mass samples. We evaluate the proposed method on the publicly available mammography dataset DDSM, demonstrating that it outperforms state-of-the-art mass detection methods.
This work is supported by the National Natural Science Foundation of China General Program 61976239.
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School of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Communication and Computer Network, South China University of Technology, Guangzhou, 510006, China
Hua Yuan, YiMao Yan & Shoubin Dong
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Democritus University of Thrace, Xanthi, Greece
Lazaros Iliadis
Democritus University of Thrace, Xanthi, Greece
Antonios Papaleonidas
Lancaster University, Lancaster, UK
Plamen Angelov
Teesside University, Middlesbrough, UK
Chrisina Jayne
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Yuan, H., Yan, Y., Dong, S. (2023). Bilateral Mammogram Mass Detection Based on Window Cross Attention. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_6
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