Volumetric medical image processing with deep learning
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One of the fundamental capabilities of deep learning is its ability of accomplishing a multitude of tasks by learning features directly from raw data instead of relying on a fixed set of features purposefully engineered by humans. Although this has brought both a simplification of the implementations and a great performance increase, most work have been limited to analysis of 2D images. Moving to the realm of 3D or even Nd images, such as those obtained when scanning the structures of the human body, brings undeniable advantages given by the ability of perceiving structures beyond the 2D image plane at the expenses of computational load and memory. When considering the content of the image in more than 2 dimensions it is also possible to improve the learning objectives to achieve a simpler and often more effective formulation. In this talk I will present my recent contributions in the field of medical image analysis with a special focus on techniques applied on signals having 3 or more dimensions.
Bio:听Fausto Milletar矛, PhD, is a lead of applied AI at Johnson and Johnson, currently focusing on problems related to surgical data science, surgical robotics and video analytics. During his PhD he has published several works in the field of computer vision applied to medicine and in particular to radiology and ultrasound imaging. His research has also impacted his work at butterfly network inc and NVIDIA corporation where he has had the opportunity of transferring some of his research into products.