Segment Anything in Pathology Images with Natural Language
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Zhixuan, Hou, Junlin, Lin, Liqi, Wang, Yihui, Bie, Yequan, Wang, Xi, Zhou, Yanning, Chan, Ronald Cheong Kin, Chen, Hao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
by: Chen, Zhixuan, et al.
Published: (2024)
by: Chen, Zhixuan, et al.
Published: (2024)
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
by: Chen, Zhixuan, et al.
Published: (2024)
by: Chen, Zhixuan, et al.
Published: (2024)
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
by: Xu, Zhe, et al.
Published: (2025)
by: Xu, Zhe, et al.
Published: (2025)
A Unified Low-level Foundation Model for Enhancing Pathology Image Quality
by: Liu, Ziyi, et al.
Published: (2025)
by: Liu, Ziyi, et al.
Published: (2025)
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
by: Bie, Yequan, et al.
Published: (2024)
by: Bie, Yequan, et al.
Published: (2024)
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
by: Hou, Junlin, et al.
Published: (2024)
by: Hou, Junlin, et al.
Published: (2024)
An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training
by: Nie, Yuxiang, et al.
Published: (2025)
by: Nie, Yuxiang, et al.
Published: (2025)
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment
by: Bie, Yequan, et al.
Published: (2024)
by: Bie, Yequan, et al.
Published: (2024)
Matte Anything: Interactive Natural Image Matting with Segment Anything Models
by: Yao, Jingfeng, et al.
Published: (2023)
by: Yao, Jingfeng, et al.
Published: (2023)
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks
by: Xie, Peng, et al.
Published: (2024)
by: Xie, Peng, et al.
Published: (2024)
MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology
by: Yang, Shu, et al.
Published: (2024)
by: Yang, Shu, et al.
Published: (2024)
Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
by: Wang, Hongmei, et al.
Published: (2024)
by: Wang, Hongmei, et al.
Published: (2024)
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
by: Xu, Yingxue, et al.
Published: (2024)
by: Xu, Yingxue, et al.
Published: (2024)
Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
by: Xu, Shuting, et al.
Published: (2025)
by: Xu, Shuting, et al.
Published: (2025)
Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning
by: Xu, Zhe, et al.
Published: (2025)
by: Xu, Zhe, et al.
Published: (2025)
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation
by: Liu, Runsheng, et al.
Published: (2024)
by: Liu, Runsheng, et al.
Published: (2024)
Segment Anything Model for Road Network Graph Extraction
by: Hetang, Congrui, et al.
Published: (2024)
by: Hetang, Congrui, et al.
Published: (2024)
Segment Anything in Medical Images
by: Ma, Jun, et al.
Published: (2023)
by: Ma, Jun, et al.
Published: (2023)
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)
Segment Anything, Even Occluded
by: Tai, Wei-En, et al.
Published: (2025)
by: Tai, Wei-En, et al.
Published: (2025)
Segment Anything Model for Medical Images?
by: Huang, Yuhao, et al.
Published: (2023)
by: Huang, Yuhao, et al.
Published: (2023)
Describe Anything in Medical Images
by: Xiao, Xi, et al.
Published: (2025)
by: Xiao, Xi, et al.
Published: (2025)
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
by: Xiong, Xinyu, et al.
Published: (2024)
by: Xiong, Xinyu, et al.
Published: (2024)
A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
by: Cai, Yu, et al.
Published: (2026)
by: Cai, Yu, et al.
Published: (2026)
Composition Vision-Language Understanding via Segment and Depth Anything Model
by: Huo, Mingxiao, et al.
Published: (2024)
by: Huo, Mingxiao, et al.
Published: (2024)
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
by: Jiang, Hao, et al.
Published: (2025)
by: Jiang, Hao, et al.
Published: (2025)
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)
RepViT-SAM: Towards Real-Time Segmenting Anything
by: Wang, Ao, et al.
Published: (2023)
by: Wang, Ao, et al.
Published: (2023)
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
by: Ma, Jiabo, et al.
Published: (2024)
by: Ma, Jiabo, et al.
Published: (2024)
A Breast Vision Pathology Foundation Model for Real-world Clinical Utility
by: Xu, Yingxue, et al.
Published: (2026)
by: Xu, Yingxue, et al.
Published: (2026)
RobustSAM: Segment Anything Robustly on Degraded Images
by: Chen, Wei-Ting, et al.
Published: (2024)
by: Chen, Wei-Ting, et al.
Published: (2024)
Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model
by: Xie, Jun, et al.
Published: (2024)
by: Xie, Jun, et al.
Published: (2024)
RegCL: Continual Adaptation of Segment Anything Model via Model Merging
by: Shu, Yuan-Chen, et al.
Published: (2025)
by: Shu, Yuan-Chen, et al.
Published: (2025)
X-SAM: From Segment Anything to Any Segmentation
by: Wang, Hao, et al.
Published: (2025)
by: Wang, Hao, et al.
Published: (2025)
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis
by: Hou, Junlin, et al.
Published: (2024)
by: Hou, Junlin, et al.
Published: (2024)
Segment and Caption Anything
by: Huang, Xiaoke, et al.
Published: (2023)
by: Huang, Xiaoke, et al.
Published: (2023)
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)
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
by: Wei, Xiaobao, et al.
Published: (2023)
by: Wei, Xiaobao, et al.
Published: (2023)
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
by: Guo, Zhengrui, et al.
Published: (2026)
by: Guo, Zhengrui, et al.
Published: (2026)
Segment Anything for Cell Tracking
by: Chen, Zhu, et al.
Published: (2025)
by: Chen, Zhu, et al.
Published: (2025)
Similar Items
-
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
by: Chen, Zhixuan, et al.
Published: (2024) -
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
by: Chen, Zhixuan, et al.
Published: (2024) -
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
by: Xu, Zhe, et al.
Published: (2025) -
A Unified Low-level Foundation Model for Enhancing Pathology Image Quality
by: Liu, Ziyi, et al.
Published: (2025) -
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
by: Bie, Yequan, et al.
Published: (2024)