Data-efficient Deep Learning for Precise Medical Image Analysis

Speaker: Dr. Xi Wang
The Chinese University of Hong Kong

Title: "Data-efficient Deep Learning for Precise Medical Image Analysis"

Date: Monday; 23 Dec 2024

Time: 3:30pm - 4:30pm

Join Zoom Meeting
https://hkust.zoom.us/j/93624265291?pwd=K21sNWNFenFYZjlTWDRPMTRwMTY5dz09

Meeting ID: 936 2426 5291
Passcode: 456088

Abstract:

Automated medical image analysis holds great significance in clinical 
practice, as it can considerably improve diagnostic efficiency and accuracy. 
However, annotating medical images is prohibitively expensive, as it is often 
highly demanding, time-consuming, and labor-intensive--particularly for 
large-sized or high-dimensional medical images. As a result, the annotations 
in collected datasets are often imperfect, meaning that images are either 
coarsely labeled or partially labeled. In this talk, I will share our work on 
weakly and semi-supervised deep learning for the analysis of large-sized or 
high-dimensional medical images. First, I will discuss several 
weakly-supervised deep learning methods that use coarse labels for 
whole-slide image and optical coherence tomography image analysis. Next, I 
will focus on semi-supervised deep learning techniques that leverage large 
amounts of unlabeled data to enhance fully supervised models, with 
applications in disease classification and organ segmentation. The class 
imbalance issue is also addressed. All of the aforementioned approaches have 
been extensively evaluated on both in-house and public datasets, achieving 
state-of-the-art performance. Finally, I will conclude by outlining future 
work, which aims to extend these studies into areas such as multi-modal 
learning, longitudinal learning, and foundation model analysis.


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Biograhpy:

Dr. Xi Wang received her Ph.D. degree in Computer Science and Engineering 
(CSE) from The Chinese University of Hong Kong (CUHK) in 2020. After that, 
she worked as a post-doctoral fellow at Stanford University and CUHK. Her 
research interests are at the intersection of computer science, medical 
science, and physics, with special emphasis on deep learning, medical image 
analysis, and complex systems, strongly driven by the goal of improving human 
health. Over the past decade, She has been dedicated to developing novel deep 
learning approaches to streamline the workflow of clinical diagnosis, 
treatment planning, and prognosis for patients with chronic diseases, cancer, 
infectious diseases, and neurological diseases with high accuracy and 
efficiency at the lowest cost. She is currently working on longitudinal 
learning based on time series data for disease progression forecasting and 
computational methodology related to single-cell and spatial genomics.