CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings. We assess the foundation model CT-CLIP as a feature extractor for pretreatment computed tomography images and clinical variables from 242 diagnosed lung cancer patients. The evaluation includes adaptation strategies
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- LinkedLinked via arxiv author · 85%Sofie Allgöwer →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Mikael Johansson →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Andreas Hallqvist →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Jonas Andersson →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Åse Johnsson →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Ida Häggström →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- LinkedLinked via arxiv author · 85%Jennifer Alvén →
“CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction”
- PossiblePossibly related (embedding) · 48%TIP12 Validation of a Multimodal Artificial Intelligence Prognostic Model in Early-Stage HR+/HER2− Breast Cancer - CancerNetwork →