{"total":43,"rows":[{"id":"2305.14961","title":"Deep Learning for Survival Analysis: A Review","created":"2023-05-24","cited_by_count":75,"area":null},{"id":"2410.01086","title":"An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes","created":"2024-10-01","cited_by_count":16,"area":null},{"id":"2202.12537","title":"An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data","created":"2022-02-25","cited_by_count":13,"area":null},{"id":"2403.10603","title":"SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast","created":"2024-03-15","cited_by_count":10,"area":null},{"id":"2405.07374","title":"Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration","created":"2024-05-12","cited_by_count":10,"area":null},{"id":"2206.10477","title":"Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee","created":"2022-06-21","cited_by_count":9,"area":null},{"id":"2208.10759","title":"Survival Mixture Density Networks","created":"2022-08-23","cited_by_count":9,"area":null},{"id":"2310.18681","title":"DySurv: dynamic deep learning model for survival analysis with conditional variational inference","created":"2023-10-28","cited_by_count":9,"area":null},{"id":"2601.22259","title":"Tabular Foundation Models Can Do Survival Analysis","created":"2026-01-29","cited_by_count":9,"area":null},{"id":"2111.08175","title":"Inverse-Weighted Survival Games","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2302.02807","title":"Federated Survival Forests","created":"2023-02-06","cited_by_count":8,"area":null},{"id":"2101.03170","title":"BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values","created":"2021-01-07","cited_by_count":7,"area":null},{"id":"2204.01702","title":"Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI","created":"2022-04-01","cited_by_count":7,"area":null},{"id":"2411.07853","title":"Evidential time-to-event prediction with calibrated uncertainty quantification","created":"2024-11-12","cited_by_count":7,"area":null},{"id":"2308.02382","title":"Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics","created":"2023-08-04","cited_by_count":6,"area":null},{"id":"2308.13104","title":"Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records","created":"2023-08-24","cited_by_count":5,"area":null},{"id":"2410.20579","title":"Toward Conditional Distribution Calibration in Survival Prediction","created":"2024-10-27","cited_by_count":5,"area":null},{"id":"2101.11935","title":"A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data","created":"2021-01-28","cited_by_count":3,"area":null},{"id":"2106.01711","title":"Lymph Node Graph Neural Networks for Cancer Metastasis Prediction","created":"2021-06-03","cited_by_count":3,"area":null},{"id":"2210.11366","title":"Deep conditional transformation models for survival analysis","created":"2022-10-20","cited_by_count":3,"area":null},{"id":"2405.05488","title":"Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation","created":"2024-05-09","cited_by_count":3,"area":null},{"id":"2605.15488","title":"SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference","created":"2026-05-15","cited_by_count":3,"area":null},{"id":"2307.03575","title":"Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data","created":"2023-07-07","cited_by_count":2,"area":null},{"id":"2512.12463","title":"Understanding Overparametrization in Survival Models through Interpolation","created":"2025-12-13","cited_by_count":2,"area":null},{"id":"2108.10365","title":"L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na\\\"ive breast cancer patients","created":"2021-08-23","cited_by_count":1,"area":null},{"id":"2403.01927","title":"Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection","created":"2024-03-04","cited_by_count":1,"area":null},{"id":"2403.13078","title":"HuLP: Human-in-the-Loop for Prognosis","created":"2024-03-19","cited_by_count":1,"area":null},{"id":"2202.05155","title":"DeepCENT: Prediction of Censored Event Time via Deep Learning","created":"2022-02-08","cited_by_count":0,"area":null},{"id":"2304.08106","title":"Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients","created":"2023-04-17","cited_by_count":0,"area":null},{"id":"2306.00034","title":"Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence","created":"2023-05-31","cited_by_count":0,"area":null},{"id":"2311.17173","title":"A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2402.10551","title":"Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information","created":"2024-02-16","cited_by_count":0,"area":null},{"id":"2409.19901","title":"SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2412.18706","title":"SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation","created":"2024-12-24","cited_by_count":0,"area":null},{"id":"2505.22641","title":"Spectral Survival Analysis","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2508.14024","title":"UNICON: UNIfied CONtinual Learning for Medical Foundational Models","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2510.03483","title":"DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2511.11212","title":"MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2512.18129","title":"TraCeR: Transformer-Based Competing Risk Analysis with Longitudinal Covariates","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2602.22179","title":"Discovering Subgroups with Exceptional Survival Characteristics","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2605.04063","title":"Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2605.16208","title":"A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2608.03017","title":"Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants","created":"2026-08-04","cited_by_count":0,"area":null}],"next_cursor":null}