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SUMMARY:Machine Learning and Computational Intelligence Techniques for Bio
 medical Image Analysis - Leonardo Rundo\, Department of Radiology\, Univer
 sity of Cambridge
DTSTART:20190118T160000Z
DTEND:20190118T170000Z
UID:TALK117496@talks.cam.ac.uk
CONTACT:Yury Korolev
DESCRIPTION:Nowadays\, the amount of heterogeneous biomedical data is incr
 easing more and more thanks to the advances in imaging acquisition modalit
 ies and high-throughput technologies. This huge information ensemble could
  overwhelm the analytic capabilities needed by physicians in their daily d
 ecision-making tasks as well as by biologists investigating complex bioche
 mical systems. Quantitative imaging methods convey scientifically and clin
 ically relevant information in prediction\, prognosis or treatment respons
 e assessment\, by also considering radiomics approaches. Therefore\, the c
 omputational analysis of medical and biological images plays a key role in
  radiology and laboratory applications. In this regard\, frameworks based 
 on advanced Machine Learning and Computational Intelligence can significan
 tly improve traditional Image Processing and Pattern Recognition approache
 s. However\, conventional Artificial Intelligence techniques must be adapt
 ed and tailored to address the unique challenges concerning biomedical ima
 ging data.\nIn this talk\, the challenges and the characteristics of the m
 ost recent methods will be introduced and discussed. I will start with som
 e practical applications exploiting classic Image Processing and Pattern R
 ecognition techniques. Afterwards\, a novel medical image enhancement meth
 od based on Genetic Algorithms will be briefly described. To conclude\, th
 e generalization capabilities of Convolutional Neural Networks in medical 
 image segmentation tasks as well as the generation of realistic medical im
 ages based on Generative Adversarial Networks will be investigated.
LOCATION:MR 11\, Centre for Mathematical Sciences
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