Survival Prediction
61 papers with code • 1 benchmarks • 1 datasets
Libraries
Use these libraries to find Survival Prediction models and implementations
Survival Analysis of Heart Failure Patients
Most implemented papers
Survival prediction using ensemble tumor segmentation and transfer learning
marianocabezas/challenges2018
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Moreover, predicting the survival of the patient using mainly imaging features, while being a desirable outcome to evaluate the treatment of the patient, it is also a difficult task.
Deep learning cardiac motion analysis for human survival prediction
UK-Digital-Heart-Project/4Dsurvival
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Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Semi-Supervised Variational Autoencoder for Survival Prediction
sveinnpalsson/semivaebrats
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In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks.
Continuous and Discrete-Time Survival Prediction with Neural Networks
havakv/pycox
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Application of discrete-time survival methods for continuous-time survival prediction is considered.
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
Large-scale benchmark study of survival prediction methods using multi-omics data
The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods.
DeepHazard: neural network for time-varying risks
Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time.
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.
