Welcome to share the paper and dataset related to medical universal model

[ljwztc]

[The paper format]
Paper title:
Author list:
Paper link:
Code link:

[The dataset format]
Dataset title:
Dataset link:
Paper link [optional]:

[yizhezhang2000]

Hi! Thanks for including our work "Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model" in this awesome list! We appreciate it! We have a Github page for this short paper, the link is "https://github.com/yizhezhang2000/SAMAug/". If possible, could you please add this Github link to the list as well? Thank you! :-)

[MrGiovanni]

[1] Qin, Ziyuan, et al. "Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study." ICLR (2023).
[2] Willemink, Martin, et al. "Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks.” Radiology: Artificial Intelligence, 2022
[3] Rasmy, Laila, et al. "Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction." NPJ digital medicine 4.1 (2021): 86.
[4] Bommasani, Rishi, et al. "On the opportunities and risks of foundation models." arXiv preprint arXiv:2108.07258 (2021).
[5] Wang, Zifeng, et al. "Medclip: Contrastive learning from unpaired medical images and text." arXiv preprint arXiv:2210.10163 (2022).
[6] Liu, Jie, et al. "CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection." arXiv preprint arXiv:2301.00785 (2023).
[7] Yi, Huahui, et al. "Towards General Purpose Medical AI: Continual Learning Medical Foundation Model." arXiv preprint arXiv:2303.06580 (2023

[GewelsJI]

Hi, @ljwztc

Thanks for sharing such a nice paper list. I would appreciate it if you can add our empirical study (SAM Struggles in Concealed Scenes--Empirical Study on "Segment Anything") of adapting SAM on three concealed scenarios (including medical data) to this section

Thanks again and hope you have a nice day.

Best,
Ge-Peng.