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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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorSofie Allgöwer →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorMikael Johansson →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorAndreas Hallqvist →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorJonas Andersson →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorÅse Johnsson →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorIda Häggström →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- Linked via arxiv authorJennifer Alvén →
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction