The paper introduces SEM‑HD, a framework that leverages longitudinal mammography history as privileged information during training to improve risk prediction while requiring only a single current exam at inference. By having a student model predict latent representations of past visits and using teacher supervision from actual longitudinal data, SEM‑HD preserves temporal modeling benefits without needing prior exams at deployment. Experiments on three cohorts and two backbone architectures show consistent gains in long‑horizon AUC and pAUC, especially in low false‑positive‑rate regions, and recover much of the performance gap to full‑history models.
arXiv:2610.16329v1 Announce Type: cross Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can be...
arXiv:2607. 11343v1 Announce Type: cross Abstract: Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection.
arXiv:2609.26443v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, dia...
Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embeddings move faster along a data-derived "cancer dire...
The study examined whether embeddings from four foundation models—Mammo-CLIP, HOPPR, MedImageInsight, and BiomedCLIP—could detect pre‑diagnostic changes in screening mammograms. Using 1,773 biopsied women and matched controls, the researchers measured the speed of movement along a data‑derived “cancer direction” in embedding space over successive screening intervals. They found that embeddings from clinically grounded models (Mammo‑CLIP, HOPPR, MedImageInsight) showed faster drift in malignant cases compared to controls, while the general biomedical model BiomedCLIP did not, indicating that foundation model embeddings can encode early tissue changes without task‑specific fine‑tuning.
arXiv:2608. 13518v1 Announce Type: new Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint.
Mr.Dec is a new Transformer‑decoder model that predicts 30‑day hospital readmission by treating each admission as a chronological sequence of daily multimodal events, integrating Electronic Health Record updates and Chest X‑ray findings. It uses disease‑specific supervised contrastive learning to shape a diagnosis‑aware latent space and preserves day‑level clinical signals that other methods often compress. Experiments on MIMIC‑IV and MIMIC‑CXR datasets show state‑of‑the‑art performance and the model can highlight "Critical Days" for actionable real‑time risk stratification.
Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time.
arXiv:2609.12834v1 Announce Type: new Abstract: Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data -- one ima...
TALON is a Temporally Aware Longitudinal framework for radiology report generation that adaptively integrates variable‑length patient histories. Its Dual‑Channel Temporal Fusion Module compares each prior examination with the current one through similarity and change channels, using channel‑specific attention and a prior‑specific gate to weigh relevance and suppress redundancy. Experiments on MIMIC‑CXR demonstrate that TALON surpasses state‑of‑the‑art methods on clinical efficacy and graph‑based metrics, with performance improving as more prior examinations are available.
The paper introduces Patient, Place, Prior (P³), an audit framework that evaluates whether a longitudinal medical imaging forecast benefits from a patient’s own imaging history (Patient), from patient‑matched spatial support (Place), and from predictive value beyond a population‑average model (Prior). It applies this framework to Cancer JEPA, a one‑step model that predicts future breast dynamic contrast‑enhanced MRI scans during neoadjuvant therapy, incorporating a lesion‑constrained neural correction trained with an occlusion‑based latent objective. Validation shows lower forecast error when the neural correction uses the patient’s own history and matched lesion maps, though the 95% interval comparing patient‑specific to population‑average corrections includes zero, illustrating that P³ distinguishes input usage from true patient‑specific predictive advantage.
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...