• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    September 22, 2020: Mr. Pham Minh Tuan (Aix-Marseille University, France), AutoEncoder-Based Feature Ranking for Alzheimer Disease Classification using PET Image

    Alzheimer Disease (AD) is becoming a major type of neurodegenerative brain disease in elderly people. Early detection and diagnosis of AD is of crucial importance for developing treatments. Fluorodeoxyglucose positron emission tomography (FDG-PET) is one of the most effective biomarkers which helps to diagnose AD early. One major challenge of PET-based classification is the very high dimensionality of image features. To address this problem, dimensionality reduction could be used. In this work we propose a method for selecting the most important features from PET images. More specifically, PET images are mapped into region of interest (ROI) using an anatomical atlas. Then multiple AutoEncoder (AE) are trained and fine-tuned with softmax. After that, the connection weights learned from AEs are used to rank ROIs according to the total contribution of ROIs to the networks. To improve the ranking results, we proposed a 2-phases feature ranking method which is able to rank and select the most important ROIs. Lastly, the topranked ROIs are then input into a support vector machine (SVM) classifier. In experiments on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the proposed method significantly improves the accuracy of the classifier when compared to other popular feature ranking methods such as: Fisher score, Tscore, Conditional Mutual Information Maximization (CMIM), Lasso.

    Speaker: Mr. Pham Minh Tuan, Aix-Marseille University

    Time: 2020-09-22 00:00:00

    Venue: Webinar – Microsoft Teams


    Pham Minh Tuan is a graduate student of University of Engineering and Technology (UET), Vietnam National University (VNU). He received a B.Sc. degree in Information Technology from UET-VNU in 2016. After that, he worked as a research engineer (Machine Learning, Data Mining) for 2 years. Currently, he is an exchange student at Aix-Marseille University and working as an intern at Fresnel Institute, in Marseille, France. His research interests include Machine Learning for Neural Imaging, Deep Learning, Data Mining for Social Networks, and Healthcare.


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    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

    February 2, 2024: Prof. Heng Ji (University of Illinois at Urbana-Champaign), Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life

    A research overview of ongoing research projects, especially focusing on two that are most related to the VinUni-UIUC Smart Health Center: (1) Misinformation Detection and Trustworthy Large Language Models; (2) Joint Natural Language and Molecule Learning for Drug Discovery. Unsurprisingly these two seemingly different research problems can be tackled with a unified approach based on […]

    January 11, 2024: Dr. Le Duc Trong (FIT-UET) Resilient Multimodal Learning for Multimodal Emotion Recognition in the Presence of Incomplete Modalities

    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]