arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
arXiv:2606. 08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints.
arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.
arXiv:2607. 28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.
arXiv:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.
arXiv:2605. 25050v2 Announce Type: replace-cross Abstract: Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets.
arXiv:2607. 25497v1 Announce Type: cross Abstract: Pathology foundation models are approaching clinical deployment, yet remain vulnerable to systematic non-biological variation across centres.
arXiv:2607. 16802v1 Announce Type: new Abstract: Deep survival models are evaluated almost exclusively by the concordance index (C-index), yet they are commonly trained using likelihood objectives such as the Cox partial likelihood, discrete-time negative log-likelihood, and DeepHit likelihood.
arXiv:2608. 16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number.
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
arXiv:2607. 28671v1 Announce Type: cross Abstract: Accurate fracture risk prediction is important for osteoporosis management, but commonly used clinical tools may not fully use information available in electronic health records (EHRs) and dual-energy X-ray absorptiometry (DXA) reports.