线上会场：

腾讯会议：886-725-803

密码：718492

报告摘要：

Deep learning algorithms have found increasing applications in the biomedical domain. However, they often manifest as inscrutable black-box models, seldom harnessing the wealth of prior knowledge available for the tasks at hand. Addressing this limitation requires the incorporation of such prior knowledge into our algorithms. During this talk, I will explore this concept by delving into two illustrative examples, one leveraging a physical model and one a physiological model. The focal point of the discussion will be the first example, which draws from my doctoral research where physics-based tomographic models have been integrated with deep learning-based regularization by alternating between model-based updates and data-driven image regularization. At the end of the talk, I will offer a glimpse into our ongoing research at the Swiss AI Lab IDSIA on EMG-based speech synthesis. Here, the deep learning architecture itself capitalizes on prior knowledge derived from a physiologically inspired latent space model of speech articulation.

报告人简介：

Stefano van Gogh is a postdoctoral researcher at the Swiss AI Lab IDSIA in Lugano since August 2023. He earned his PhD at ETH Zürich, while conducting research at the Paul Scherrer Institute. His doctoral thesis focused on the development of hybrid tomographic reconstruction algorithms, combining data-driven techniques with physics-based models, designed for a cutting-edge breast imaging technology called Grating Interferometry CT. Before obtaining his PhD, he received a MSc in Biomedical Engineering and a BSc in Health Sciences and Technology, both from ETH Zürich. Stefano's primary research interests center around the seamless integration of data-driven algorithms with mechanistic models (physical and physiological), in the realm of biomedical engineering.

“论坛”主请人联系方式：

王振天：wangzhentian@tsinghua.edu.cn