Publication


Disclaimer: All the material listed and presented here is to promote the dissemination of scholarly and technical work in a timely fashion. All rights as well as the copyright are retained by authors and organizers/publishers.


Journal Articles

  1. Leveraging uncertainty information from deep neural networks for disease detection, C. Leibig, V. Allken, M.S. Ayhan, P. Berens, and S. Wahl [bioRxiv] [accepted]
  2. Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data, M.S. Ayhan, V.V. Raghavan and Alzheimer’s Disease Neuroimaging Initiative (ADNI) [arXiv]
  3. Exploitation of 3D Stereotactic Surface Projection for Predictive Modeling of Alzheimer’s Disease, M.S. Ayhan, R.G. Benton, V.V. Raghavan and S. Choubey, International Journal of Data Mining and Bioinformatics, 2013, Vol.7, No.2 [.pdf]

Conference and Workshop Papers

  1. Evaluation of Autoencoders for Bases to Represent Neuroimaging Data, A. Gupta, M.S. Ayhan, A.S. Maida, Workshop on Machine Learning and Interpretation in NeuroImaging, The 27th Annual Conference on Neural Information Processing Systems
    December 2013, Lake Tahoe, NV, USA [.pdf]
  2. Composite Kernels for Automatic Relevance Determination in Computerized Diagnosis of Alzheimer’s Disease, M.S. Ayhan, R.G. Benton, V.V. Raghavan and S. Choubey, The 2013 International Conference on Brain and Health Informatics
    October 2013, Maebashi City, GUNMA, JAPAN [.pdf]
  3. Natural Image Bases to Represent Neuroimaging Data, A. Gupta, M.S. Ayhan, A.S. Maida, Deep Learning and Neuroscience Track, The 30th International Conference on Machine Learning
    June 2013, Atlanta, GA, USA [.pdf] [supplement]
  4. Towards Indefinite Gaussian Processes, M.S. Ayhan, C.H. Chu, The Modern Nonparametric Methods in Machine Learning Workshop The 26th Annual Conference on Neural Information Processing Systems
    December 2012, Lake Tahoe, NV, USA [.pdf]
  5. Utilization of Domain-Knowledge for Simplicity and Comprehensibility in Predictive Modeling of Alzheimer’s Disease, M.S. Ayhan, R.G. Benton, V.V. Raghavan and S. Choubey, International Workshop on Multiscale Biomedical Imaging Analysis
    The IEEE International Conference on Bioinformatics and Biomedicine 2012
    October, 2012, Philadelphia, PA, USA [.pdf]
  6. Exploitation of 3D Stereotactic Surface Projection for Automated Classification of Alzheimer’s Disease According to Dementia Levels, M.S. Ayhan, R.G. Benton, V.V. Raghavan and S. Choubey, The IEEE International Conference on Bioinformatics and Biomedicine 2010 [.pdf]
    December, 2010, Hong Kong, SAR, CHINA
  7. Determining Relevant Features Based on 3D Stereotactic Surface Projection to Detect Dementia Caused by Alzheimer’s Disease, M.S. Ayhan, R.G. Benton, V.V. Raghavan and S. Choubey
    The 7th Annual Biotechnology and Bioinformatics Symposium (extended abstract)
    October 2010, Lafayette, LA, USA
  8. Comparison of Spatial Indexing Methods, M.S Ayhan, H. Sever, H. Gurcay and S. Ak
    The National Conference on Geographical Information Systems (in Turkish)
    November 2007, Trabzon, TURKEY

Theses

  1. A Probabilistic Biomarker for Alzheimer’s Disease, Dissertation, University of Louisiana at Lafayette, Advisor: Vijay Raghavan, May 2015
  2. Comparison of Spatial Indexing Methods, Master’s thesis, Baskent University, Advisor: Hayri Sever, February 2007


Work in Progress

  1. Learning Distributed Representations from the Relevant Regions of Brain via Autoencoders, Title says it all 🙂
  2. Indefinite Gaussian Processes, Follow-up to the NIPS 2012 workshop paper. This time, working on a joint optimization procedure to accomplish the Gaussian Process learning and kernel transformation/spectrum modification simultaneously.