Survival Prediction
29 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
pykao/Modified-3D-UNet-Pytorch
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Quantitative analysis of brain tumors is critical for clinical decision making.
Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data
Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
Personalized Survival Prediction with Contextual Explanation Networks
alshedivat/cen
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Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices.
Countdown Regression: Sharp and Calibrated Survival Predictions
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
pykao/BraTS2018-tumor-segmentation
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For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data.
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.
