Page 133 - AIH-2-4
P. 133
Artificial Intelligence in Health RefSAM3D for medical image segmentation
References 16. Zhang Y, Jiao R. Towards Segment Anything Model (sam) for
Medical Image Segmentation: A Survey. arXiv preprint arXiv:
1. Obuchowicz R, Strzelecki M, Piorkowski A. Clinical 2305.03678; 2023.
applications of artificial intelligence in medical imaging
and image processing-A review. Cancers (Basel). 17. Ma J, He Y, Li F, Han L, You C, Wang B. Segment anything in
2024;16(10):1870. medical images. Nat Commun. 2024;15(1):654.
doi: 10.3390/cancers16101870 doi: 10.1038/s41467-024-44824-z.
2. Addimulam S, Mohammed MA, Karanam RK, et al. 18. Shaharabany T, Dahan A, Giryes R, Wolf L. Autosam:
Deep learning-enhanced image segmentation for medical Adapting Sam to Medical Images by Overloading the Prompt
diagnostics. Malays J Med Biol Res. 2020;7(2):145-152. Encoder. arXiv preprint arXiv: 2306.06370; 2023.
3. Khalifa M, Albadawy M. AI in diagnostic imaging: 19. Na S, Guo Y, Jiang F, Ma H, Huang J. Segment any Cell:
Revolutionising accuracy and efficiency. In: Computer A Sam-Based Auto-Prompting Finetuning Framework for
Methods and Programs in Biomedicine Update. Vol. 5; 2024. Nuclei Segmentation. arXiv preprint arXiv: 2401.13220; 2024.
4. Kirillov A, Mintun E, Ravi N, et al. Segment anything. In: 20. Min B, Ross H, Sulem E, et al. Recent advances in natural
Proceedings of the IEEE/CVF International Conference on language processing via large pre-trained language models:
Computer Vision; 2023. p. 4015-4026. a survey. ACM Comput Surv. 2024;57(1):1-45.
5. Zou X, Yang J, Zhang H, et al. Segment Everything Everywhere doi: 10.1145/3605943
all at Once. arXiv Preprint arXiv: 2304.06718; 2023.
21. Radford A, Kim JW, Hallacy C, et al. Learning transferable
6. Huang Y, Yang X, Liu L, et al. Segment anything model for visual models from natural language supervision. arXiv
medical images? Med Image Anal. 2024;92:103061. preprint arXiv:2103.00020;2021.
doi: 10.1016/j.media.2023.103061 22. Jia C, Yang Y, Xia Y, et al. Scaling up visual and vision-
language representation learning with noisy text supervision.
7. Hu EJ, Shen Y, Wallis P, et al. Lora: Low-rank adaptation of
large language models. arXiv preprint:2106.09685, 2021. arXiv preprint arXiv:2102.05918; 2021.
8. Poth C, Sterz H, Paul I, et al. Adapters: A unified library 23. Zou X, Yang J, Zhang H, et al. Segment everything
for parameter-efficient and modular transfer learning. In: everywhere all at once. In: Oh A, Naumann T, Globerson A,
Feng Y, Lefever E, editors. Proceedings of the 2023 Conference Saenko K, Hardt M, Levine S, editors. Advances in Neural
on Empirical Methods in Natural Language Processing, Information Processing Systems 36: Annual Conference on
EMNLP 2023 - System Demonstrations, Singapore; 2023. Neural Information Processing Systems 2023, NeurIPS 2023,
p. 149-160. New Orleans, LA, USA; 2023.
9. Shen J, Wang W, Chen C, et al. Medtuning: A New 24. Wang X, Zhang X, Cao Y, Wang W, Shen C, Huang T. Seggpt:
Parameter-efficient Tuning Framework for Medical Volumetric Segmenting Everything in Context. arXiv Preprint arXiv:
Segmentation. arXiv Preprint arXiv: 2304.10880; 2024. 2304.03284; 2023.
10. Zhang K, Liu D. Customized Segment Anything Model 25. Oquab M, Darcet T, Moutakanni T. Dinov2: Learning Robust
for Medical Image Segmentation. arXiv preprint arXiv: Visual Features without Supervision. arXiv Preprint arXiv:
2304.13785; 2023. 2304.07193; 2024.
11. Wang H, Guo S, Ye J, et al. Sam-med3d: Towards General- 26. Wang Y, Zhou W, Mao Y, Li H. Detect any Shadow: Segment
purpose Segmentation Models for Volumetric Medical Images. Anything for Video Shadow Detection. arXiv preprint arXiv:
arXiv preprint arXiv: 2310.15161; 2024. 2305.16698; 2023.
12. Wu J, Ji W, Liu Y, et al. Medical Sam Adapter: Adapting 27. Deng R, Cui C, Liu Q, et al. Segment Anything Model (sam)
Segment Anything Model for Medical Image Segmentation. for Digital Pathology: Assess Zero-Shot Segmentation on
arXiv preprint arXiv: 2304.12620; 2023. Whole Slide Imaging. arXiv preprint arXiv: 2304.04155; 2023.
13. Gong S, Zhong Y, Ma W, et al. 3dsamadapter: Holistic 28. He S, Bao R, Li J, et al. Accuracy of Segmentanything Model
adaptation of sam from 2d to 3d for promptable tumor (sam) in Medical Image Segmentation Tasks. arXiv preprint
segmentation. Med Image Anal. 2024;98:103324. arXiv: 2304.09324; 2023.
14. Xie B, Tang H, Duan B, Cai D, Yan Y. Masksam: Towards 29. Hu C, Li X. When Sam Meets Medical Images: An
Auto-prompt Sam with Mask Classification for Medical Image Investigation of Segment Anything Model (Sam) on Multi-
Segmentation. arXiv preprint arXiv: 2403.14103; 2024. Phase Liver Tumor Segmentation. arXiv preprint arXiv:
2304.08506; 2023.
15. Li C, Khanduri P, Qiang Y, Sultan RI, Chetty I, Zhu D.
Autoprosam: Automated Prompting Sam for 3d Multi-Organ 30. Zhou T, Zhang Y, Zhou Y, Wu Y, Gong C. Can Sam Segment
Segmentation. arXiv preprint arXiv: 2308.14936; 2024. Polyps? arXiv preprint arXiv: 2304.07583; 2023.
Volume 2 Issue 4 (2025) 127 doi: 10.36922/AIH025080010

