arXiv:2606.04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundation models like the Segment Anything Model (SAM) show promise, they often struggle with medical images without specific adaptation. Moreover, point prompts, despite being the most natural form of user interaction, provide insufficient spatial context for reliable segmentation, particularly when target structures are irregular or poorly contrasted. In this paper, we propose an enhanced segmentation
The rapid advancement of foundation models like SAM is driving efforts to adapt them for specialized domains such as medical imaging, where data scarcity and variability pose significant challenges.
Improving medical image segmentation through enhanced AI models can significantly impact diagnostic accuracy, treatment planning, and efficiency in healthcare, leading to better patient outcomes.
The proposed method improves the reliability and practicality of using foundation models for medical image analysis, making them more adaptable to real-world clinical scenarios with less manual prompting.
- · Healthcare sector
- · Medical AI companies
- · Patients
- · Radiologists
- · Traditional image analysis software
- · Manual segmentation techniques
More accurate and faster medical image analysis will become widely accessible.
Reduced physician workload and improved diagnostic consistency will free up resources and enhance clinical throughput.
The integration of such AI tools could lead to new paradigms in personalized medicine and early disease detection across various medical specialties.
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Read at arXiv cs.AI