Abstract
Motivated by the fact that the contralateral mammograms can provide the symmetrical difference of the left and right breasts to assist identify the breast cancer, we propose a bilateral residual neural network (Bi-ResNet) that can automatically classify the normality/abnormality based on the unregistered contralateral whole mammograms. Specifically, the parallel ResNet network is designed to simultaneously process a group of contralateral mammograms and respectively capture the discriminative representations from the left and right mammograms, and the concatenation strategy in the final is used to integrate the differentiated features for the abnormal classification task. The proposed Bi-ResNet can achieve reproducible and similar results based on different backbones and is superior to traditional contralateral analysis methods in both automation and performance. Finally, our proposed Bi-ResNet is greatly demonstrated on the publicly available DDSM dataset, a total of 10480 images, yielding the highest AUC of 0.908 on the abnormal classification task. Through the massive experiments, we deem our model is stable and robust, and has the potential to be recommended to clinical application in the future.
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Acknowledgements
This work is jointly supported by the National Natural Science Foundation of China (Nos. 61175012 and 61201421) and Natural Science Foundation of Gansu Province (No. 18JR3RA288).
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Wang, R., Guo, Y., Wang, W., Ma, Y. (2019). Bi-ResNet: Fully Automated Classification of Unregistered Contralateral Mammograms. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_28
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