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.

By Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata
arXiv Computer Vision
Oct 9

Revisiting Integration of Image and Metadata for DICOM Series Classification: Cross-Attention and Dictionary Learning

The paper presents an end‑to‑end multimodal framework for classifying DICOM image series that jointly processes image content and acquisition metadata. It uses modality‑aware encoders, a bi‑directional cross‑modal attention mechanism, and a sparse, missingness‑aware dictionary‑based encoder for metadata, avoiding imputation. The method handles variable series length with a 2.5D visual encoder and demonstrates superior performance on the Duke Liver MRI dataset and a large multi‑institutional cohort, outperforming image‑only, metadata‑only, and other multimodal baselines.

By Tuan Truong, Melanie Dohmen, Sara Lorio, Matthias Lenga