Chapter title |
Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries
|
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Chapter number | 9 |
Book title |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
|
Published in |
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : third International Workshop, BrainLes 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised selected papers. Bra..., January 2018
|
DOI | 10.1007/978-3-319-75238-9_9 |
Pubmed ID | |
Book ISBNs |
978-3-31-975237-2, 978-3-31-975238-9
|
Authors |
Matt Higger, Martha Shenton, Sylvain Bouix |
Abstract |
Because mild Traumatic Brain Injuries (mTBI) are heterogeneous, classification methods perform outlier detection from a model of healthy tissue. Such a model is challenging to construct. Instead, we utilize region-specific pairwise (person-to-person) comparisons. Each person-region is characterized by a distribution of Fractional Anisotropy and comparisons are made via Median, Mean, Bhattacharya and Kullback-Liebler distances. Additionally, we examine an ordinal decision rule which compares a subject's nth most atypical region to a healthy control's. Ordinal comparison is motivated by mTBI's heterogeneity; each mTBI has some set of damaged tissue which is not necessarily spatially consistent. These improvements correctly distinguish Persistent Post-Concussive Symptoms in a small dataset but achieve only a .74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 6 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor | 1 | 17% |
Student > Ph. D. Student | 1 | 17% |
Student > Bachelor | 1 | 17% |
Other | 1 | 17% |
Unknown | 2 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 2 | 33% |
Psychology | 1 | 17% |
Unknown | 3 | 50% |