NEWS
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Senior Projects Our senior students have completed their CMPE 491 Graduation project presentations. Please feel free to enjoy their project materials. http://www.cmpe.boun.edu.tr/undergraduate/ Read more... |
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"Abbas Güçlü" team wins first slot car race The First Slot Car Race was held on January 15th, 2016. The contestants are the senior students taking CmpE443: Principles of Embedded Systems Design. Out of 21 groups, 6 groups Read more... |
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Software Engineering Projects Our senior students have completed their long CMPE 352-451 Software Engineering Project Development Marathon. Please feel free to enjoy their project materials: https://www. Read more... |
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PhD Defense Day: 3 in 1... Dear CmpE Members,11.12.2015 has been a remarkable day for CmpE, as we had 3 PhD presentations! ------------------------AUTOMATIC SYNTHETIC BENCHMARK GENERATION FOR MULTICORE Read more... |
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CmpE Team wins award at the EmotiW Challenge A CmpE team, composed of Heysem Kaya, Furkan Gürpınar, Sadaf Afshar, and Albert Ali Salah got the second place in the video based emotion recognition category of the 3. Emotion Read more... |
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CmpE Events
Tomorrow
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CmpE MS Thesis Defense: Investigating Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Latent Space Manipulation by Deniz Ayvaz
- Start time: 10:00am, Friday, September 22nd
- End time: 11:00am, Friday, September 22nd
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Title: Investigating Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Latent Space ManipulationSpeaker: Deniz AyvazAbstract: Alzheimer's disease (AD), a progressive neurologic disorder, is the most common cause of dementia, affecting millions worldwide. It severely affects cognitive abilities, such as speech and memory. Due to its multi-modal effects, the disease has a heterogeneous progression pattern, which makes tracking the patient's cognitive impairment level challenging. Mild Cognitive Impairment (MCI) is considered an intermediate stage before AD. Early prediction of the conversion from MCI to AD is crucial to taking necessary precautions for decelerating the disease progression and developing suitable treatments. The problem of early prediction of AD among MCI patients has often been posed as a classification problem. However, the classification models may not be sufficient to disclose the underlying factors of the transition from MCI to AD. This thesis proposes an intuitive framework to investigate the underlying causes and risk factors of the conversion from MCI to AD. The proposed deep learning-based framework employs latent space manipulation techniques to obtain principal directions toward AD diagnosis for an MCI patient and analyze the changes the patient's attributes undergo during disease progression. The predictive ability of the proposed framework is evaluated by correlating the magnitude of the manipulation in the latent space of a variational auto-encoder trained with MCI and AD patients with the possibility of conversion in later stages. Experimental results show promising quantitative and qualitative outcomes on two publicly available and commonly used AD neuroimaging datasets.
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