Assoc. Prof. Chisako Muramatsu
Shiga University, Japan
Chisako Muramatsu received the B.S. degree in Health Sciences from Kanazawa University in 2001 and Ph.D. degree in Medical Physics from the University of Chicago in 2008. She became a visiting associate professor in Department of Intelligent Image Information, Graduate School of Medicine, Gifu University in 2012 and in Faculty of Engineering, Gifu University in 2017. She is an associate professor in Faculty of Data Science, Shiga University since 2019. She serves as program committee members of Computer-Aided Diagnosis Conference of SPIE Medical Imaging and International Workshop on Breast Imaging.
Asst. Prof. Jiaqing Liu
Ritsumeikan University, Japan
Jiaqing Liu received the B.E. degree from Northeastern University, Shenyang, China, in 2016, and the M.E. and D.E. degrees from Ritsumeikan University, Kyoto, Japan, in 2018 and 2021, respectively. From 2020 to 2021, he was a JSPS Research Fellowship for Young Science. From October 2021 to March 2022, he was a Specially Appointed Assistant Professor with the Department of Intelligent Media, ISIR, Osaka University, Osaka, Japan. He is currently an Assistant Professor with the College of Information Science and Engineering, Ritsumeikan University. His research interests include pattern recognition, image processing, and machine learning.
Speech Title: "Multimodal Deep Learning in Depression Estimation"
Abstract: Deep learning has been successfully applied in many research fields, such as computer vision, speech recognition and natural language processing. Most of them are focused on single modality. On the other hand, multimodal information is more useful for practical applications. Multimodal deep learning has got a lot of attention and becomes an important issue in the field of artificial intelligence. Compared with traditional single-modal deep learning, there are following challenges in multimodal deep learning: development of multimodal dataset; multimodal representation; multimodal alignment; multimodal translation and multimodal co-learning. The propose of this talk is to introduce efficient and accurate multimodal deep learning methods and apply them to depression estimation.
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