Assistant Professor Jiaqing Liu
Ritsumeikan University, Japan
Biography:
Jiaqing Liu received his
B.E. degree from Northeastern University,
China, in 2016. He then earned both his M.E.
(2018) and D.E. (2021) degrees from
Ritsumeikan University in Kyoto, Japan. From
2020 to 2021, he was a JSPS Research Fellow
for Young Scientists. He subsequently served
as a Specially Appointed Assistant Professor
at the Department of Intelligent Media,
Institute of Scientific and Industrial
Research (ISIR), Osaka University, from
October 2021 to March 2022. He is currently
an Assistant Professor at the College of
Information Science and Engineering,
Ritsumeikan University. His research interests include
computer vision, medical engineering, and
deep learning.
Speech Title: Multimodal AI for
Depression Detection: Advances in Emotion
Understanding and Model Interpretability
Abstract: Depression is a prevalent mental
health condition that significantly affects
quality of life and social functioning.
Despite rapid progress in artificial
intelligence, current large language models
(LLMs) face challenges in integrating
multimodal information and offering
interpretable emotional insights. In this
invited talk, I will present a novel
prefix-tuning approach that enables
efficient adaptation of emotion-pretrained
LLMs for depression detection. Our method
models multimodal inputs—such as speech,
facial expressions, and text—while tuning
only a small subset of parameters. This
approach enhances emotional awareness,
cross-modal generalization, and model
interpretability. It highlights the
potential of multimodal AI for mental health
applications and lays the groundwork for
future emotionally adaptive human-AI
interaction systems.
Associate Professor Yuan Ye
Shenyang University of Technology, China
Biography:
Yuan Ye currently serves as an Associate
Professor in the Software College of
Shenyang University of Technology in China.
She received her B.E. degree and Master's
degree from the Zhejiang University, China,
and the University of New South Wales,
Australia, in 2010 and 2012, respectively.
She obtained her Ph.D. from the Northeastern
University, China, in 2022, and had academic
exchanges at the Ritsumeikan University,
Japan, during 2014-2016. Professor Yuan's
primary research interests lie in the fields
of image segmentation, 3D reconstruction,
and machine learning. She has authored one
academic monograph and contributed numerous
papers to international journals and
conferences, two of which have been
published in IEEE Transactions on Image
Processing.
Speech Title: An Experience-driven
Interpretable Multi-task Model for
Segmentation and Classification of Small
Cell Lung Cancer and Non-small Cell Lung
Cancer from CT Images
Abstract: Accurate
identification of small and non-small cell
lung cancer classes holds significant
importance in clinical practice. An
experience-driven interpretable multi-task
network to concurrently perform segmentation
and classification of small cell and
non-small cell lung cancer is introduced.
The core architecture of this multi-task
model is based on StarNet, featuring a
shared feature extraction branch and
task-specific decoding branches for tumor
segmentation and classification. Leveraging
clinical knowledge of small cell lung cancer
characteristics, such as indistinct edges,
tissue invasion, and limited large cavity
areas, two auxiliary branches are proposed:
edge uncertainty estimation and tumor core
area reconstruction. The values from edge
uncertainty estimation and reconstruction
integrity estimation are utilized in the
classification branch to facilitate small
cell lung cancer classification.
Furthermore, to enhanced interpretability,
bottleneck layer features are extracted for
comparative learning, and a three-level
contrastive loss is proposed to improve the
differentiation of disease features. Lastly,
an interpretable strategy based on trained
feature query matching is presented,
providing radiologists with clinical
insights and reference images while the
model outputs recognition predictions.