Abstract
Detection of the asymmetry (AS) and architectural distortion (AD) on mammograms is important for early breast cancer diagnosis. However, this is a challenging task because there are very limited mammography data containing these two lesions. In this paper, we tackle this problem by presenting a novel transfer learning framework of Supervised mass-Transferred Pre-training (STP) followed by Supervised Constrained Contrastive Fine-tuning (S\(\mathrm C^2\)F). While STP can leverage the commonly available mass data to help with detecting the rarely available AS and AD as pre-training, S\(\mathrm C^2\)F can depart the mass, AS, and AD in the embedding space as far as possible with a carefully designed constrained contrastive loss. In addition, a novel detection network - AsAdNet, is proposed for the AS and AD detection. The validation results on the largest-so-far AS and AD dataset show state-of-the-art (SOTA) detection performance.
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Notes
- 1.
This study was approved by the ethics and institutional review board.
- 2.
Our cooperating medical institutes also agree with this definition.
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Cao, Z. et al. (2022). Asymmetry and Architectural Distortion Detection with Limited Mammography Data. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_16
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