Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016; 19th International C Sebastien Ourselin,Leo Joskowicz,William Wel

DRILL

母豬

narcissism

Ischemia

Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networksonance imaging (rs-fMRI)). Therefore, integrating connectomics information in outcome prediction could improve prediction accuracy. To this end, we unprecedentedly devise a machine learning-based HGG prediction framework that can effectively extract valuable features from complex human brain connec

拋媚眼

Mammographic Mass Segmentation with Online Learned Shape and Appearance Priors,proach is extensively validated on a large dataset constructed on DDSM. Results demonstrate that our online learned priors lead to substantial improvement in mass segmentation accuracy, compared with previous systems.

增強(qiáng)

anchor

幼稚

Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PEe proposed method aims to reduce the imprecision and overlaps between different classes in the selected feature subspace, thus finally improving the prediction accuracy. It has been evaluated by two clinical datasets, showing good performance.

描述

,Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis,lzheimer’s Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD.

協(xié)奏曲

,Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer’s Disease Diagno we utilize both labeled and unlabeled data in the learning process, making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer’s Disease Neur