NEWS
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Prof. Cem Ersoy has been elected as a principal member of the Academy of Sciences Congratulations to Prof. Cem Ersoy, who has been elected as a principal member of the Academy of Sciences. We have no doubt that Prof. Cem Ersoy, who sets an example for all of Read more... |
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The Turkish Language Processing Platform (TULAP) by Computer Engineering and Linguistics departments is released TULAP (Turkish Language Processing Platform) is a software platform that includes natural language processing tools and datasets for Turkish. It has been developed in the scope of Read more... |
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Lale Akarun is Elected Vice President of IAPR, International Association of Pattern Recognition The International Conference of Pattern Recognition (ICPR), organized by the Association of Pattern Recognition (IAPR) was held in Montreal, Canada, during August 22-25, 2022. Read more... |
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CoLoRs Research Group at Osaka University for the The Lifelong Robot Learning Project Professor Minoru Asada, administrative director of the Symbiotic Intelligent Systems Center (SisReC) of the Institute for Open and Transdisciplinary Research Initiatives, Read more... |
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Nanonetworking Research Group Collaboration with Yonsei University, Korea Dr. H. Birkan Yılmaz, a member of the Nanonetworking Research Group (NRG) at the Department of Computer Engineering is conducting a TUBITAK project titled Molecular 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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