Large Vision-Language Models (LVLMs) have made remarkable progress across visual perception tasks, yet spatial reasoning remains a persistent weakness, especially for questions that require reasoning over visual space. Recent spatial-reasoning methods incorporate generated grounding, where models predict bounding boxes...
Rafi Ibn Sultan, Xiang-Yu Zhou, Mohammad O. S. Chowdhury et al.· 0 citations
NA-UNETR effectively balances local precision and global context, enabling accurate segmentation of thin, low-contrast LAD structures and demonstrates a robust and computationally efficient framework for substructure-level cardiac segmentation in radiotherapy planning.
Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou et al.· Medical Physics (Lancaster)· 1 citation
Manual organ‐at‐risk (OAR) delineation takes 20–40 min per case, a major bottleneck within the 50–90 min treatment window of abdominal MR‐guided adaptive radiotherapy (MRgRT). Most deep learning systems adopt single‐fraction approaches that discard valuable temporal context from prior treatment fractions.
Chengyin Li, D. Rusu, Rafi Ibn Sultan et al.· Medical Physics (Lancaster)· 0 citations
This work introduces MedPlex (Medical Plexus of Vision and Language), an end-to-end VLM framework that makes text guidance a continuous, clinically grounded component of segmentation learning, and introduces Bi-Fusion (Bidirectional Fusion), visual and textual representations evolve jointly across the encoding hierarch...
Rafi Ibn Sultan, Hui Zhu, Chengyin Li et al.· 0 citations
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