Abstract
Due to the rapid increase incidence of breast cancer, automated breast volume scanner (ABVS) is developed to detect breast cancer rapidly and accurately, which can automatically scan the whole breast with less manual operation. However, it is challenging for clinicians to segment the tumor region and further identify the benign and malignant tumors from the ABVS images since it has the large image size and low data quality. For this reason, we propose an effective 3D deep convolutional neural network for multi-task learning from ABVS data. Specifically, a new VNet structure is designed using deep attentive module for performance boosting. In addition, a semi-supervised mechanism is introduced to address the issue of insufficient labeled training data. Due to the difference of the tumor size, we create a two-stage process and fit the small size tumor via volume refinement block for further performance improvement. The experimental results on our self-collected data demonstrate that our model has achieved Dice coefficient of 0.764 for 3D segmentation and F1-score of 81.0% for classification. Our network outperforms the related algorithm as well.
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Acknowledgements
This work was supported partly by National Natural Science Foundation of China (Nos. 62001302 and 61871274), Key Laboratory of Medical Image Processing of Guangdong Province (No. 2017B030314133), Guangdong Basic and Applied Basic Research Foundation (Nos. 2021A1515011348, 2019A1515111205), and Shenzhen Key Basic Research Project (Nos. JCYJ20170818 094109846, JCYJ20190808145011259, RCBS20200714114920379).
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Liu, Y., Yang, Y., Jiang, W., Wang, T., Lei, B. (2021). Semi-supervised Attention-Guided VNet for Breast Cancer Detection via Multi-task Learning. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_45
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DOI: https://doi.org/10.1007/978-3-030-87358-5_45
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