A time-aware ResNet-based model on PET/CT images improves overall survival prediction in NSCLC by incorporating temporal data, achieving 4.3% higher AUC than fixed-time baselines.
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks.Proceedings of the AAAI Conference on Artificial Intelligence, 32(1)
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Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
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Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
A time-aware ResNet-based model on PET/CT images improves overall survival prediction in NSCLC by incorporating temporal data, achieving 4.3% higher AUC than fixed-time baselines.
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
- KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis