Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Yichi, Hu, Shiyao, Ren, Sijie, Jiang, Chen, Cheng, Yuan, Qi, Yuan |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
by: Zhang, Yichi, et al.
Published: (2023)
by: Zhang, Yichi, et al.
Published: (2023)
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
by: Zhang, Yichi, et al.
Published: (2024)
by: Zhang, Yichi, et al.
Published: (2024)
Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation
by: Miao, Juzheng, et al.
Published: (2024)
by: Miao, Juzheng, et al.
Published: (2024)
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
by: Wei, Zhikai, et al.
Published: (2024)
by: Wei, Zhikai, et al.
Published: (2024)
Learning to Prompt Segment Anything Models
by: Huang, Jiaxing, et al.
Published: (2024)
by: Huang, Jiaxing, et al.
Published: (2024)
SAIF: A Stability-Aware Inference Framework for Medical Image Segmentation with Segment Anything Model
by: Wu, Ke, et al.
Published: (2026)
by: Wu, Ke, et al.
Published: (2026)
Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model
by: Huang, Shiqi, et al.
Published: (2025)
by: Huang, Shiqi, et al.
Published: (2025)
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
by: Wu, Junde, et al.
Published: (2023)
by: Wu, Junde, et al.
Published: (2023)
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
by: Zou, Ke, et al.
Published: (2023)
by: Zou, Ke, et al.
Published: (2023)
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
Segment Anything Model for Medical Images?
by: Huang, Yuhao, et al.
Published: (2023)
by: Huang, Yuhao, et al.
Published: (2023)
SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
by: Yan, Zhiling, et al.
Published: (2025)
by: Yan, Zhiling, et al.
Published: (2025)
SAMSA 2.0: Prompting Segment Anything with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation
by: Roddan, Alfie, et al.
Published: (2025)
by: Roddan, Alfie, et al.
Published: (2025)
Uncovering Modality Discrepancy and Generalization Illusion for General-Purpose 3D Medical Segmentation
by: Zhang, Yichi, et al.
Published: (2026)
by: Zhang, Yichi, et al.
Published: (2026)
SamLP: A Customized Segment Anything Model for License Plate Detection
by: Ding, Haoxuan, et al.
Published: (2024)
by: Ding, Haoxuan, et al.
Published: (2024)
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
by: Chen, Tianrun, et al.
Published: (2025)
by: Chen, Tianrun, et al.
Published: (2025)
A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation
by: Ward, Tyler, et al.
Published: (2025)
by: Ward, Tyler, et al.
Published: (2025)
DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation
by: Liu, Xinyu, et al.
Published: (2024)
by: Liu, Xinyu, et al.
Published: (2024)
Segment Anything in Medical Images
by: Ma, Jun, et al.
Published: (2023)
by: Ma, Jun, et al.
Published: (2023)
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
by: Gao, Yifan, et al.
Published: (2023)
by: Gao, Yifan, et al.
Published: (2023)
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
by: Barfoot, Theodore, et al.
Published: (2025)
by: Barfoot, Theodore, et al.
Published: (2025)
Part-aware Prompted Segment Anything Model for Adaptive Segmentation
by: Zhao, Chenhui, et al.
Published: (2024)
by: Zhao, Chenhui, et al.
Published: (2024)
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
by: Wei, Xiaobao, et al.
Published: (2023)
by: Wei, Xiaobao, et al.
Published: (2023)
DEAP-3DSAM: Decoder Enhanced and Auto Prompt SAM for 3D Medical Image Segmentation
by: Chen, Fangda, et al.
Published: (2025)
by: Chen, Fangda, et al.
Published: (2025)
S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
by: Paranjape, Jay N., et al.
Published: (2024)
by: Paranjape, Jay N., et al.
Published: (2024)
SAMSA: Segment Anything Model Enhanced with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation
by: Roddan, Alfie, et al.
Published: (2025)
by: Roddan, Alfie, et al.
Published: (2025)
Multi-rater Prompting for Ambiguous Medical Image Segmentation
by: Wang, Jinhong, et al.
Published: (2024)
by: Wang, Jinhong, et al.
Published: (2024)
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
by: Huai, Zheang, et al.
Published: (2025)
by: Huai, Zheang, et al.
Published: (2025)
Medical SAM 2: Segment medical images as video via Segment Anything Model 2
by: Zhu, Jiayuan, et al.
Published: (2024)
by: Zhu, Jiayuan, et al.
Published: (2024)
pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation
by: Wang, Tong, et al.
Published: (2025)
by: Wang, Tong, et al.
Published: (2025)
EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything
by: Song, Joonhyeon, et al.
Published: (2024)
by: Song, Joonhyeon, et al.
Published: (2024)
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
by: Zhang, Yuxuan, et al.
Published: (2024)
by: Zhang, Yuxuan, et al.
Published: (2024)
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
by: Lin, Weifeng, et al.
Published: (2025)
by: Lin, Weifeng, et al.
Published: (2025)
Benchmarking Human and Automated Prompting in the Segment Anything Model
by: Quesada, Jorge, et al.
Published: (2024)
by: Quesada, Jorge, et al.
Published: (2024)
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
by: Wang, Tianyang, et al.
Published: (2025)
by: Wang, Tianyang, et al.
Published: (2025)
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
by: Jiang, Yidong
Published: (2025)
by: Jiang, Yidong
Published: (2025)
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
by: Feng, Chen-Bin, et al.
Published: (2024)
by: Feng, Chen-Bin, et al.
Published: (2024)
Skip and Skip: Segmenting Medical Images with Prompts
by: Chen, Jiawei, et al.
Published: (2024)
by: Chen, Jiawei, et al.
Published: (2024)
Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis
by: Yuan, Cheng, et al.
Published: (2024)
by: Yuan, Cheng, et al.
Published: (2024)
Similar Items
-
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
by: Zhang, Yichi, et al.
Published: (2023) -
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
by: Zhang, Yichi, et al.
Published: (2024) -
Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation
by: Miao, Juzheng, et al.
Published: (2024) -
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
by: Wei, Zhikai, et al.
Published: (2024) -
Learning to Prompt Segment Anything Models
by: Huang, Jiaxing, et al.
Published: (2024)