arXiv Machine Learning
Multi-Modal Tumor Survival Prediction via Graph-Guided Mixture of Experts
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arXiv Computer Vision
Sep 7
Real-World Multi-Modal and Longitudinal Lung Cancer Dataset
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 Computer Vision
Sep 1
Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
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...
arXiv AI
Jul 20
Large Language Models as Unified Multimodal Learners for Clinical Prediction
arXiv:2607. 15380v1 Announce Type: cross Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities.
arXiv AI
Jul 21
AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis
arXiv:2607. 16260v1 Announce Type: cross Abstract: Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis.
arXiv Machine Learning
Jun 18
ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis
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 Machine Learning
Jun 2
OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.