arXiv:2511.12110v5 Announce Type: replace-cross Abstract: Despite notable progress in text-guided medical image segmentation nowadays, these methods are limited to single-round dialogues and fail to support multi-round reasoning, which is important for medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), a new task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressively develop their understanding of medical knowledge. To support this task, we const
The proliferation of advanced AI in medical imaging necessitates more sophisticated interaction paradigms, moving beyond single-query systems to enable multi-round, entity-level reasoning for educational and diagnostic purposes.
This development represents a significant step towards more human-like, interactive AI in specialized domains, potentially transforming medical education, training, and the future of diagnostic AI tools.
AI segmentation in medical images can now handle complex, multi-round dialogues and entity-level reasoning, allowing for progressive understanding and refinement.
- · Medical AI developers
- · Medical educators
- · Healthcare technology companies
- · Medical students and trainees
- · Legacy single-query segmentation software
- · Companies without multi-modal AI expertise
Improved and more accessible medical education tools leveraging interactive AI for image interpretation.
Accelerated development of AI-assisted diagnostic systems capable of complex reasoning and iterative analysis with clinicians.
The integration of such interactive AI systems into real-time surgical guidance or telemedicine platforms, augmenting human expertise dynamically.
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Read at arXiv cs.AI