arXiv:2610.33054v1 Announce Type: new
Abstract: Simulating an individual's disease course requires connecting patient history to future events, clinical timing and disease morphology. Longitudinal pr...
By Youcheng Li (Peking University, Isoplex Lab), Fa Tian (Isoplex Lab), Binbin Su (Peking Union Medical College, Isoplex Lab)
The paper presents M2-OPMDNet, a multimodal deep learning framework that combines white‑light and autofluorescence intraoral images with structured clinical data to detect oral potentially malignant disorders (OPMDs). Using a prospectively collected real‑world dataset, the model achieved an AUC of 0.952, outperforming unimodal approaches and improving detection of visually subtle lesions. Explainability was provided through SHAP analysis, showing that clinical variables significantly contributed to risk estimation alongside imaging features.
By Ruilin You, Yihan Wang, Jiabin Chen, Cherie Wink, Petra Wilder-Smith, Rongguang Liang, Bofan Song
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
By Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti
arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
By Mingi Hong
Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content. Such control is essential because clinical scenarios diverge: emergency triage prioritizes sensitivity to reduce missed findings, whereas confirmatory interpretation emphasizes specificity to limit unnecessary interventions.
arXiv:2608. 10505v1 Announce Type: new Abstract: Automated radiology report generation is advancing rapidly in response to the shortage of radiologists, yet unlike a perception model, existing generation models offer no control over the sensitivity-specificity trade-off of their diagnostic content.
By Ying Jin, Noel C. F. Codella, John Corring, Mu Wei, Dinei Florencio, Eric Horvitz