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New lung cancer dataset integrates imaging, clinical, and genomic data

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new multi-modal dataset for lung cancer research, designed to mimic real-world data complexities. This dataset includes imaging from whole-slide images, CT scans, and PET scans, alongside clinical, transcriptomic, and longitudinal follow-up information for 1,365 patients. The data exhibits significant missingness across modalities, making it suitable for studying robust multi-modal fusion strategies. Initial benchmarks on survival prediction tasks demonstrate that integrating these diverse data sources improves predictive performance, even with substantial missing data. AI

IMPACT This dataset could accelerate research into multi-modal AI for medical diagnosis and prognosis by providing a realistic, complex data foundation.

RANK_REASON The cluster describes a new dataset and benchmark published on arXiv, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New lung cancer dataset integrates imaging, clinical, and genomic data

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The cluster describes a new dataset and benchmark published on arXiv, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata ·

    Real-World Multi-Modal and Longitudinal Lung Cancer Dataset

    arXiv:2609.05202v1 Announce Type: cross Abstract: Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous data sources such as medical imaging, clinical records, and genomics to improve predictive performance and support clinica…

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