Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer Using a Non-proportional Hazards Model.
Alexander KatzmannAlexander MühlbergMichael SühlingDominik NörenbergStefan MaurusJulian Walter HolchVolker HeinemannHorst-Michael GroßPublished in: PRIME@MICCAI (2019)
Keyphrases
- proportional hazards model
- computed tomography
- colorectal cancer
- variable selection
- survival analysis
- breast cancer
- ct images
- survival prediction
- survival data
- ct scans
- image reconstruction
- medical imaging
- medical images
- three dimensional
- ct data
- computed tomography scans
- cross validation
- regression model
- linear program
- high dimensional
- lung cancer
- computer vision
- logistic regression
- medical image analysis
- dimension reduction
- domain knowledge
- x ray
- gene selection
- clinical data
- machine learning
- image registration
- anatomical structures
- gene expression
- magnetic resonance images
- deformable models
- image guided
- dimensionality reduction
- predictive modeling
- evolutionary computing
- image data
- data mining methods
- high quality
- feature selection
- neural network model