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Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification (2306.10676v1)

Published 19 Jun 2023 in cs.CV and cs.LG

Abstract: Mammogram image is important for breast cancer screening, and typically obtained in a dual-view form, i.e., cranio-caudal (CC) and mediolateral oblique (MLO), to provide complementary information. However, previous methods mostly learn features from the two views independently, which violates the clinical knowledge and ignores the importance of dual-view correlation. In this paper, we propose a dual-view correlation hybrid attention network (DCHA-Net) for robust holistic mammogram classification. Specifically, DCHA-Net is carefully designed to extract and reinvent deep features for the two views, and meanwhile to maximize the underlying correlations between them. A hybrid attention module, consisting of local relation and non-local attention blocks, is proposed to alleviate the spatial misalignment of the paired views in the correlation maximization. A dual-view correlation loss is introduced to maximize the feature similarity between corresponding strip-like regions with equal distance to the chest wall, motivated by the fact that their features represent the same breast tissues, and thus should be highly-correlated. Experimental results on two public datasets, i.e., INbreast and CBIS-DDSM, demonstrate that DCHA-Net can well preserve and maximize feature correlations across views, and thus outperforms the state-of-the-arts for classifying a whole mammogram as malignant or not.

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References (34)
  1. Dv-dcnn: Dual-view deep convolutional neural network for matching detected masses in mammograms. Computer methods and programs in biomedicine, 207:106152, 2021.
  2. Representation learning for mammography mass lesion classification with convolutional neural networks. Computer methods and programs in biomedicine, 127:248–257, 2016.
  3. Breast cancer detected with screening us: reasons for nondetection at mammography. Radiology, 270(2):369–377, 2014.
  4. Multi-view probabilistic classification of breast microcalcifications. IEEE Transactions on medical imaging, 35(2):645–653, 2015.
  5. Supervised contrastive pre-training formammographic triage screening models. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VII 24, pages 129–139. Springer, 2021.
  6. Unregistered multiview mammogram analysis with pre-trained deep learning models. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 652–660. Springer, 2015.
  7. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE transactions on medical imaging, 36(11):2355–2365, 2017.
  8. The automated learning of deep features for breast mass classification from mammograms. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 106–114. Springer, 2016.
  9. Inbreast-database masses characterization. XXIII CBEB, 2012.
  10. A support vector machine approach for detection of microcalcifications. IEEE transactions on medical imaging, 21(12):1552–1563, 2002.
  11. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  12. Sigurdur Helgason and S Helgason. The radon transform, volume 2. Springer, 1980.
  13. Early dissemination seeds metastasis in breast cancer. Nature, 540(7634):552–558, 2016.
  14. Local relation networks for image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3464–3473, 2019.
  15. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific data, 4(1):1–9, 2017.
  16. Deep multiinstance mammogram classification with region label assignment strategy and metric-based optimization. IEEE Transactions on Cognitive and Developmental Systems, 14(4):1717–1728, 2021.
  17. Breast imaging reporting and data system (bi-rads). Radiologic Clinics, 40(3):409–430, 2002.
  18. Machine learning–based radiomics for molecular subtyping of gliomas. Clinical Cancer Research, 24(18):4429–4436, 2018.
  19. Cross-view relation networks for mammogram mass detection. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 8632–8638. IEEE, 2021.
  20. Inbreast: toward a full-field digital mammographic database. Academic radiology, 19(2):236–248, 2012.
  21. The impact of mammographic screening on breast cancer mortality in europe: a review of trend studies. Journal of medical screening, 19(1_suppl):26–32, 2012.
  22. Breast cancer diagnosis in two-view mammography using end-to-end trained efficientnet-based convolutional network. Ieee Access, 10:77723–77731, 2022.
  23. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
  24. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626, 2017.
  25. Deep generative breast cancer screening and diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 859–867. Springer, 2018.
  26. Deep neural networks with region-based pooling structures for mammographic image classification. IEEE Transactions on Medical Imaging, 39(6):2246–2255, 2020.
  27. Attention is all you need. In Advances in neural information processing systems, pages 5998–6008, 2017.
  28. Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7794–7803, 2018.
  29. Towards robust dual-view transformation via densifying sparse supervision for mammography lesion matching. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24, pages 355–365. Springer, 2021.
  30. Towards improved breast mass detection using dual-view mammogram matching. Medical image analysis, 71:102083, 2021.
  31. Deep active learning for dual-view mammogram analysis. In Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, pages 180–189. Springer, 2021.
  32. Deep correlational learning for survival prediction from multi-modality data. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 406–414. Springer, 2017.
  33. Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation. In International conference on medical image computing and computer-assisted intervention, pages 155–163. Springer, 2019.
  34. Deep multi-instance networks with sparse label assignment for whole mammogram classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 603–611. Springer, 2017.
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