Cover Image

Curriculum Prompting Foundation Models for Medical Image Segmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zheng, Xiuqi, Zhang, Yuhang, Zhang, Haoran, Liang, Hongrui, Bao, Xueqi, Jiang, Zhuqing, Lao, Qicheng
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation
by: Jiang, Chongcong, et al.
Published: (2026)

TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
by: Jiang, Zekun, et al.
Published: (2024)

Generalizable Facial Expression Recognition
by: Zhang, Yuhang, et al.
Published: (2024)

Boosting Medical Image Classification with Segmentation Foundation Model
by: Gu, Pengfei, et al.
Published: (2024)

Confounder-Aware Medical Data Selection for Fine-Tuning Pretrained Vision Models
by: Ji, Anyang, et al.
Published: (2025)

Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model
by: Zhang, Xinwei, et al.
Published: (2025)

Robust Box Prompt based SAM for Medical Image Segmentation
by: Huang, Yuhao, et al.
Published: (2024)

ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
by: Ayzenberg, Lev, et al.
Published: (2024)

Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation
by: Tang, Jiaqi, et al.
Published: (2026)

CTS: A Consistency-Based Medical Image Segmentation Model
by: Zhang, Kejia, et al.
Published: (2024)

PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
by: Yan, Zhonghao, et al.
Published: (2025)

MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
by: Xun, Siyi, et al.
Published: (2025)

Unified Medical Image Segmentation with State Space Modeling Snake
by: Zhang, Ruicheng, et al.
Published: (2025)

TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation
by: Wang, Ranmin, et al.
Published: (2024)

Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation
by: Yang, Guangjing, et al.
Published: (2026)

K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
by: Guo, Bangwei, et al.
Published: (2025)

SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation
by: Du, Xiaogang, et al.
Published: (2026)

ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation
by: Elgebaly, Aya, et al.
Published: (2025)

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
by: Chen, Yulin, et al.
Published: (2024)

Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment
by: Lu, Haisheng, et al.
Published: (2024)

Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning
by: Kim, Hyung-Il, et al.
Published: (2024)

Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
by: Xu, Yiming, et al.
Published: (2026)

A Foundation Model for General Moving Object Segmentation in Medical Images
by: Yan, Zhongnuo, et al.
Published: (2023)

Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
by: Zhou, Zhongliang, et al.
Published: (2024)

Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
by: Zhu, Yun, et al.
Published: (2025)

Improving Generalization of Medical Image Registration Foundation Model
by: Hu, Jing, et al.
Published: (2025)

DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis
by: Saeed, Numan, et al.
Published: (2025)

RAP: Retrieve, Adapt, and Prompt-Fit for Training-Free Few-Shot Medical Image Segmentation
by: Mao, Zhihao, et al.
Published: (2026)

AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models
by: Li, Xingjian, et al.
Published: (2025)

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
by: Jiang, Yidong
Published: (2025)

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
by: Deng, Zhongying, et al.
Published: (2026)

MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
by: Liu, Shengyuan, et al.
Published: (2026)

Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation
by: Fischer, Stefan M., et al.
Published: (2025)

Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning
by: Zhou, Pengcheng, et al.
Published: (2025)

Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
by: Liu, Tianyi, et al.
Published: (2024)

Diffusion Curriculum: Synthetic-to-Real Data Curriculum via Image-Guided Diffusion
by: Liang, Yijun, et al.
Published: (2024)

Few-Shot Medical Image Segmentation with High-Fidelity Prototypes
by: Tang, Song, et al.
Published: (2024)

X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
by: Sun, Zeyi, et al.
Published: (2024)

Diffusion Model in Latent Space for Medical Image Segmentation Task
by: Ngoc, Huynh Trinh, et al.
Published: (2025)

Unleashing Diffusion and State Space Models for Medical Image Segmentation
by: Wu, Rong, et al.
Published: (2025)