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INCONVENIENT is introduced, a benchmark designed to study robustness to convenience-sample bias and provides explicit control over both the direction and severity of relationship shifts, enabling a systematic assessment of robustness to convenience sampling.
Radiotherapy planning is time‐intensive, iterative, and operator‐dependent. Locally deployed large language‐model agents may support planning automation while avoiding transmission of patient data to external model providers.
Large language model agents can adjust optimization parameters in a treatment planning system under natural-language instruction, but no study has tested a model post-trained for extended reasoning against a general-purpose model in stereotactic radiosurgery. We compared a reasoning-optimized model (QwQ-32B) with a...
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.
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.
Deployed vision-language systems often gate their answers on confidence, making confidence robustness relevant to oversight. We study confidence readouts under white-box, image-only attacks constrained to preserve the generated answer byte-identically. Under a reachability assumption, an unmovable readout cannot outper...
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