arXiv:2607. 16260v1 Announce Type: cross Abstract: Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
MIST is a multimodal survival prediction framework that fuses whole-slide images and genomic profiles by representing genomic features as tokens that query histology context tokens derived from a foundation model. The architecture enriches molecular information with histology context before survival prediction, avoiding late-stage merging of separately encoded modalities. Training incorporates discrete-time survival prediction, genomic feature masking, WSI dropout, and contrastive alignment, and demonstrates improved external C-index across colon, renal, lung, and glioblastoma cohorts compared to standard fusion baselines.
SCOUT is a concept‑grounded multimodal transformer that generates whole‑slide pathology reports by integrating local histological patterns, whole‑slide context, and expert‑curated diagnostic concepts. It uses evolving visual representations and recursively updated slide‑ and concept‑conditioned representations, with separate attention pathways during decoding that are fused adaptively for each token. Evaluated on TCGA‑BRCA, HistAI, and REG‑2025, SCOUT outperformed existing methods, improving BLEU, METEOR, and ROUGE‑L scores and raising the Clinical Report Quality Score on REG‑2025.
arXiv:2606. 19140v1 Announce Type: new Abstract: Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data.
arXiv:2609.14072v1 Announce Type: new Abstract: Large Language Models (LLMs) have displayed impressive capabilities in handling tasks that require few demonstration examples, making them effective fe...
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.
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
The paper presents a newly curated, multi-center, multi-modal, and longitudinal lung cancer dataset comprising 1,365 patients with whole-slide images, CT scans, PET scans, structured clinical data, transcriptomics, and follow-up information. The dataset features substantial, non-uniform missingness across modalities, making it ideal for evaluating robust multi-modal fusion strategies. Benchmarks on 12‑month overall survival, disease‑specific survival, and longitudinal hazard prediction demonstrate that integrating complementary modalities consistently outperforms uni-modal approaches, even under severe missing data.
arXiv:2609.24894v1 Announce Type: cross Abstract: Whole-slide pathology images (WSIs) contain gigapixel-scale visual content, creating a major scalability challenge for slide-level multimodal large l...
arXiv:2606. 15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift.
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv:2606. 19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis.