Towards Accurate, Interpretable, and Privacy-preserving AI for Healthcare

Speaker:        Dr. Xiaoxiao Li
                Computer Science Department
                Princeton University

Title:          "Towards Accurate, Interpretable, and Privacy-preserving
                 AI for Healthcare"

Date:           Monday, 18 January 2021

Time:           10:00am - 11:00am


Zoom Meeting:
https://hkust.zoom.us/j/465698645?pwd=c2E4VTE3b2lEYnBXcyt4VXJITXRIdz09

Meeting ID:     465 698 645
Passcode:       20202021


Abstract:

Recent progress in artificial intelligence (AI) has advanced our ability
to analyze biomedical data. However, significant obstacles, such as a lack
of model transparency and insufficient training samples, have hindered
applying AI to practical healthcare applications. To fill the gaps between
AI and healthcare, improving the trustworthiness of AI is desired. In this
talk, I will present the progress of developing the next generation of
trustworthy AI systems for healthcare applications. Initially, we design
accurate deep learning models for disease classification and investigate
how to perform computational biomarker discovery from a model
interpretation perspective. Later, we improve the efficiency and quantify
the uncertainly of Shapley value-based feature importance interpretation
methods. Our further work explores multi-site learning under data non-IID
assumption and proposes a privacy-preserving federated learning framework
with built-in novel domain adaptation methods. We validate our methods for
characterizing Autism spectrum disorder (ASD) using functional magnetic
resonance imaging (fMRI). Our results demonstrate that it is promising to
utilize advanced deep learning models, novel model explanation methods,
and federated learning to boost neuroimage analysis performance. Our
approaches bring new hope for accelerating deep learning applications in
estimating image-derived biomarkers and the healthcare field in general.


********************
Biograhy:

Dr. Xiaoxiao Li is a postdoctoral research fellow in the Computer Science
Department at Princeton University. She obtained her Ph.D. degree in
Biomedical Engineering from Yale University in 2020. During her Ph.D., she
was awarded the Advanced Graduate Leadership Fellowship. She received the
honored Bachelor's Degree at Chu Kochen Honors College, Zhejiang
University in 2015. Her research interest lies in the interdisciplinary
field of artificial intelligence and biomedical analysis, aiming to
improve the AI systems' trustworthiness for healthcare applications. She
has published nearly 30 papers at machine learning and medical imaging
conferences and journals, such as MICCAI, IPMI, ICML, and Medical Image
Analysis. Her work has received OHBM Merit Abstract Award, MIML Best Paper
Award, and DART Best Paper Award.