Three Short Talks from Prof. Daniel Weller's Group

Title: How does the mouse brain work for spatial recognition?
Speaker: Haoyi Liang

Abstract: Numerous computer vision algorithms are proposed to teach computers to understand their environments, and artificial intelligence approaches, such as convolutional neural networks, show promising results. However, do animals perceive their environment in a similar way as AI? Our work with the Department of Neuroscience sheds light on this question by studying how mice perceive their environment.

Title: Smarter tuning of image processing algorithms through machine learning
Speaker: Tanjin Toma

Abstract: In image and video processing, algorithms for inverse problems (e.g., image enhancement, image reconstruction) often have some parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error. But, manual tuning of parameters is time-consuming as well as impractical when multiple parameters exist in a problem. In this talk, I’ll discuss how machine learning can be exploited to automatically choose such parameters effectively.

Title: Robust myocardial T1 mapping with convolutional neural networks
Speaker: Haris Jeelani

Abstract: In cardiac magnetic resonance imaging, the T1 relaxation time in myocardial tissue may be used as an indicator for a variety of pathological conditions. A pixel-wise non-linear regression model is typically used to obtain T1 maps. In this talk we discuss our approach of obtaining T1 maps using convolutional neural networks that are more robust than the conventional regression method.