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Kendall, A. and Gal, Y., “What uncertainties do we need in bayesian deep learning for computer vision?,” Advances in neural information processing systems
2017
Earlier work this paper cites.
Xu, Y., Gong, M., Xie, S., and Batmanghelich, K., “Box-adapt: Domain-adaptive medical image segmentation using bounding boxsupervision,” arXiv preprint arXiv:2108.08432
Original
2021
Earlier work this paper cites.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al., “Segment anything,” arXiv preprint arXiv:2304.02643
Original
2023
Earlier work this paper cites.
Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., and Zheng, F., “Track anything: Segment anything meets videos,” arXiv preprint arXiv:2304.11968
Original
2023
Earlier work this paper cites.
Yu, T., Feng, R., Feng, R., Liu, J., Jin, X., Zeng, W., and Chen, Z., “Inpaint anything: Segment anything meets image inpainting,” arXiv preprint arXiv:2304.06790
Original
2023
Earlier work this paper cites.
Li, F., Zhang, H., Sun, P., Zou, X., Liu, S., Yang, J., Li, C., Zhang, L., and Gao, J., “Semantic-sam: Segment and recognize anything at any granularity,” arXiv preprint arXiv:2307.04767
Original
2023
Earlier work this paper cites.

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2023

False Negative/Positive Control for SAM on Noisy Medical Images

Yao, Xing, Liu, Han, Hu, Dewei et al.

Understand

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation.

It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images.
We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation.
The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.

Built on

Kendall, A. and Gal, Y., “What uncertainties do we need in bayesian deep learning for computer vision?,” Advances in neural information processing systems
2017
Earlier work this paper cites.
Xu, Y., Gong, M., Xie, S., and Batmanghelich, K., “Box-adapt: Domain-adaptive medical image segmentation using bounding boxsupervision,” arXiv preprint arXiv:2108.08432
Original
2021
Earlier work this paper cites.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al., “Segment anything,” arXiv preprint arXiv:2304.02643
Original
2023
Earlier work this paper cites.
Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., and Zheng, F., “Track anything: Segment anything meets videos,” arXiv preprint arXiv:2304.11968
Original
2023
Earlier work this paper cites.
Yu, T., Feng, R., Feng, R., Liu, J., Jin, X., Zeng, W., and Chen, Z., “Inpaint anything: Segment anything meets image inpainting,” arXiv preprint arXiv:2304.06790
Original
2023
Earlier work this paper cites.
Li, F., Zhang, H., Sun, P., Zou, X., Liu, S., Yang, J., Li, C., Zhang, L., and Gao, J., “Semantic-sam: Segment and recognize anything at any granularity,” arXiv preprint arXiv:2307.04767
Original
2023
Earlier work this paper cites.

Similar

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2023

Cited alongside, same era.

Wu, J., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., Jin, Y., and Arbel, T., “Medical sam adapter: Adapting segment anything model for medical image segmentation,” arXiv preprint arXiv:2304.12620

Original

2023

Cited alongside, same era.

Zhang, Y., Zhou, T., Liang, P., and Chen, D. Z., “Input augmentation with sam: Boosting medical image segmentation with segmentation foundation model,” arXiv preprint arXiv:2304.11332

Original

2023

Cited alongside, same era.

Shin, H., Kim, H., Kim, S., Jun, Y., Eo, T., and Hwang, D., “Sdc-uda: Volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation,” in [ Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

2023

Closest in time.

Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., and Li, K., “Sam on medical images: A comprehensive study on three prompt modes,” arXiv preprint arXiv:2305.00035

Original

2023

Closest in time.