Published in Breast Imaging: Diagnosis and Intervention, 2022
This review summarizes current advancements and challenges in applying artificial intelligence to breast imaging, including detection, diagnosis, and risk assessment.
Recommended citation: Wang, Xin, et al. "Artificial Intelligence in Breast Imaging." *Breast Imaging: Diagnosis and Intervention*, Springer, 2022, pp. 435–453.
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Published in Biomedical Signal Processing and Control, 2023
This paper introduces the concept of “2.75 D” to enhance learning performance on small 3D medical imaging datasets by transforming them into rich 2D feature representations.
Recommended citation: Wang, Xin, et al. "2.75 D: Boosting Learning by Representing 3D Medical Imaging to 2D Features for Small Data." *Biomedical Signal Processing and Control*, vol. 84, 2023, p. 104858. Elsevier.
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Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
This work introduces Disasymnet, a self-adversarial learning framework that disentangles asymmetrical abnormalities in bilateral mammograms for improved breast cancer analysis.
Recommended citation: Wang, Xin, et al. "Disasymnet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-Adversarial Learning." *MICCAI 2023*, pp. 57–67. Springer.
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Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024
This paper proposes an ordinal longitudinal attention model to predict future breast cancer events from sequential mammograms.
Recommended citation: Wang, Xin, et al. "Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms." *MICCAI 2024*, pp. 155–165. Springer.
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Published in npj Breast Cancer, 2025
This study explores the prediction of short-to long-term breast cancer risk using longitudinal mammographic screening history.
Recommended citation: Wang, Xin, et al. "Predicting short-to long-term breast cancer risk from longitudinal mammographic screening history." *npj Breast Cancer*, vol. 11, no. 1, p. 118, 2025.
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Published in Nature Communications, 2025
This paper introduces Mammo-AGE, a deep learning model that estimates biological breast age from mammograms.
Recommended citation: Wang, Xin, et al. "Mammo-AGE: deep learning estimation of breast age from mammograms." *Nature Communications*, vol. 16, no. 1, p. 10934, 2025.
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