ICDM2009

Internal Report for Threshold Classifiers

Results of the Minoshima inspired classifiers.

Heterogeneous Data Fusion for Alzheimer's Disease Study

 

Assessment of Three-class Diagnostic Tests When Disease Verification is Subject to Selection

Relevant - sort of! Aiming at a three class problem. Based on the Mini-Mental State Examination. Only simulation study done. Note, they mention 3-states of Alzheimer, but don't list them. I'm aware of 5 (normal, quest, mild, mod, severe). Published in 2008;

AUTOMATIC CLASSIFICATION OF ALZHEIMER’S DISEASE VS. FRONTOTEMPORAL DEMENTIA: A SPATIAL DECISION TREE APPROACH WITH FDG-PET

Classification - Minoshima an author - How to separate Alzheimer's patients from patients with Frontotemporal dementia.

Data Exploration with Paired Hierarchical Visualizations: Initial Designs of PairTrees

Deals with Visualization - Should reference this to Zonghuan, Shixian, Vijay and Henry on their visualization project.

Automated Segmentation and Volumetric Analysis of Brain Components on MR Imaging

Possible use to the image processing aspect of the project. A 'preprocessing' paper.

Fast Data Anonymization with Low Information Loss

Information Hiding - Very relevant to Vijay and Tom info hide work. Something we may to keep in mind if we start looking to 'share' data.

Learning Subspace Kernels for Classification

Not related - false retrieval. Useful, though, in that it discusses using Kernel methods with Sub-space discovery. NOTE: References this paper which may be useful: J. Ye and et al. Heterogeneous data fusion and analysis for alzheimer’s disease study. In KDD, 2008

Online Selection of Discriminative Tracking Features

A paper for Object Tracking - may be useful for future work. Note: References this paper which may be useful: Y. Liu et al., “Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer’s Disease Classification,” Proc. Seventh Int’l Conf. Medical Image Computing and Computer Aided Intervention, pp. 393-401, Oct. 2004.

Integrating Naive Bayes and FOIL

The 'Alzheimer' is drug related. Probably good to know about the classifier, but otherwise not relevant.

Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis

Feature Selection in Images. Some notes on detecting differences in Alzh versus normal; but, nothing 'formal'. More, a note that there seems to be a difference.

Thin Structure Segmentation and Visualization in Three-Dimensional Biomedical Images: A Shape-Based Approach

Mention Alzheimer as an motivation, but not about it. Aimed at finding 'lines' and thin sheets within 3D biomedical images.

Characterization of Neuropathological Shape Deformations

Image processing. Does try to explain why shape deform needed using Alz, normal, schizophrenia, and normal-pressure hydrocephalus patients. Possible use as a feature set, but doesn't address our problem. NOTE: Track down this reference: T. Sandor, M. Albert, J. Stafford, and S. Harpley, “Use of Computerized CT Analysis to Discriminate Between Alzheimer Patients and Normal Control Subjects,” Amer. J. Neuroradiology, vol. 9, pp. 1,181-1,187, Nov./Dec. 1988.

Biomedical Informatics Research Network: Integrating Multi-Site Neuroimaging Data Acquisition, Data Sharing and Brain Morphometric Processing

Image Processing Pipeline looking for coorelates between Mild Alzheimer and Mild Cognitive Impairment. More focus on the processing and registering. Should probably look for more recent work by these folks.

Visualisation of Clinical and Non-Clinical Characteristics of Patients with Behavioural and Psychological Symptoms of Dementia

Visualization - How to explain behaviour and management of patients with Alzheimer's - covers different levels/symptoms.

Multicenter Standardized F-DDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementias

 

FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease