A8 - Front-End Vision and Deep Learning
|This course will discuss in detail modern findings in the neurophysiology, connectivity and functionality of the visual system, the best studied brain function today. And we deeply look into the mathematical background of Deep Learning.We give insight in modern approaches to deep convolutional neural nets, and we will teach a number of well-established mathematical modeling techniques in detail, in particular multi-scale and multi-orientation differential geometry, geometric reasoning, models for self-organization and plasticity, and geometric neural feedback, leading to effective adaptive operations. We present the theory in an axiomatic, intuitive and fundamentally understood way.
We discuss modern brain imaging methods at all scales, and innovative models for human and computational vision, such as the role of retinal stellate amacrine cells, on-off channels, colorization and temporal processing. The course is interspersed with working and powerful applications in medical image analysis, such as computer-aided detection of breast tumors, polyp detection in 3D virtual colonoscopy, deblurring, invariant feature detection, adaptive geometry-driven diffusion, development of retinal vessel biomarkers, and contextual Gestalt-based operators to deal with missing data.
For more detailed information please see: www.frontendvision.net/2018/
|The goal is to develop and better understand both highly effective medical computer-aided diagnosis systems, as well as modern models for visual perception.|
|Each morning of 3 lectures is followed by an afternoon computer lab (all software code is supplied). We exercise with all developed notions, exploiting the high-level ‘play and design’ functionality of Wolfram’s Mathematica 11.|