Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms

Author:

Xiong Xinyu,Wang Churan,Li Wenxue,Li Guanbin

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Chai, S., et al.: Ladder fine-tuning approach for sam integrating complementary network. arXiv preprint arXiv:2306.12737 (2023)

2. Chen, T., et al.: Sam fails to segment anything?-sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more. arXiv preprint arXiv:2304.09148 (2023)

3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

4. He, S., Bao, R., Li, J., Grant, P.E., Ou, Y.: Accuracy of segment-anything model (sam) in medical image segmentation tasks. arXiv preprint arXiv:2304.09324 (2023)

5. Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: ICML, pp. 2790–2799. PMLR (2019)

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