Survival Prediction
52 papers with code • 1 benchmarks • 1 datasets
Survival Analysis of Heart Failure Patients
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.
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
mungomeng/survival-deepmts
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However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g., local lymph node metastasis and adjacent tissue invasion).
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages.
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.
