arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2605. 20297v2 Announce Type: replace-cross Abstract: Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning.
arXiv:2607. 14116v1 Announce Type: cross Abstract: Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language.
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.
arXiv:2512. 14157v2 Announce Type: replace Abstract: Recent medical MLLMs have made significant progress in generating step-by-step textual reasoning chains.
arXiv:2607. 04344v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular surface diseases (OSDs) is severely hindered by a paucity of high-fidelity, multimodal instruction-tuning data.
arXiv:2607. 24743v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment.