Abstract Details

Title Applying Generative Adversarial Network on Structural Brain MRI for Unsupervised Classification of Headache

Topic Headache

Presentation(s) P5 - Poster Session 5 (5:30 PM-6:30 PM)

Poster/Presentation
Number 12-003

Objective

This study aims to employ Brainomaly, an unsupervised detection approach, for classifying headache patients using unannotated T1-weighted brain MRIs. We introduce a pseudo-AUC metric for inference model selection without the need for annotated data.

Background Existing methods depend on annotated data for accurate headache detection. As labeled data are scarce, we propose Brainomaly, a Generative Adversarial Network-based approach that identifies structural abnormalities indicative of headaches, even without labeled data.

Design/Methods Brainomaly employs a generator network trained on two sets of brain MRIs: a combination of unannotated MRIs from headache patients and healthy controls and another set comprising solely MRIs from healthy controls. Through adversarial feedback from a discriminator network, the generator learns to represent 'healthy' brains and generate high-quality MRIs resembling healthy brains. The generated MRIs are subtracted from input MRIs to reveal structural deviations, aiding headache detection. We calculate the average value of the difference map as a headache detection score, where higher values indicate a higher likelihood of the MRI originating from a headache patient. To address model selection for inference, we propose the pseudo-AUC (AUCp) metric, assuming annotations for "unannotated mixed MRIs" as "headache," while known annotations are used for "healthy MRIs."

Results

The dataset comprises 96 migraine, 48 acute post-traumatic headache, and 49 persistent post-traumatic headache patients, along with 532 healthy controls. Average age (headache 39.9+/-11.8 years, HC 41.6+/-12.7 years, P=0.1) and sex (P=0.1) did not differ between groups. Brainomaly achieved an average headache detection AUC (true) of 0.8960, surpassing existing unsupervised methods. Applying our AUCp metric to the second-best method, HealthyGAN, improved its AUC from 0.7695 to 0.8088. State-of-the-art methods such as ALAD, Ganomaly, and DDAD achieved lower AUCs of 0.6955, 0.6913, and 0.6280, respectively.

Conclusions These results prove Brainomaly's superiority in headache detection over existing methods. The proposed AUCp metric can select a better model for inference.

Authors/Disclosures
Md Mahfuzur Rahman Siddiquee (Arizona State University) PRESENTER	Md Mahfuzur Rahman Siddiquee has received personal compensation for serving as an employee of Meta.
Jay Shah, MD (Arizona State University)	Mr. Shah has nothing to disclose.
Todd J. Schwedt, MD, F�鶹��ýӳ�� (Mayo Clinic)	Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Lundbeck. The institution of Dr. Schwedt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Abbvie. Dr. Schwedt has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Salvia. Dr. Schwedt has or had stock in Nocira.Dr. Schwedt has or had stock in Allevalux. The institution of Dr. Schwedt has received research support from National Institutes of Health. The institution of Dr. Schwedt has received research support from United States Department of Defense. The institution of Dr. Schwedt has received research support from Patient Centered Outcomes Research Institute. The institution of Dr. Schwedt has received research support from Henry Jackson Foundation. The institution of Dr. Schwedt has received research support from Pfizer. The institution of Dr. Schwedt has received research support from National Headache Foundation. The institution of Dr. Schwedt has received research support from American Heart Association. The institution of Dr. Schwedt has received research support from AbbVie. The institution of Dr. Schwedt has received research support from Flinn Foundation. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received intellectual property interests from a discovery or technology relating to health care. Dr. Schwedt has received publishing royalties from a publication relating to health care.
Catherine D. Chong, PhD, F�鶹��ýӳ��	Dr. Chong has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for HCOP.
Baoxin Li (Arizona State University, School of Computing and Augmented Intelligence)	Baoxin Li has nothing to disclose.
Teresa Wu (Arizona State University)	Teresa Wu has nothing to disclose.

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