Medical Imaging Summer School (MISS) 2016

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source: zammù multimedia - Università di Catania
Medical imaging is the science and technology to acquire images of the human body (either as a whole or in parts) for clinical interpretation or interventions. The main challenge for clinicians lies in the explosive number of images being acquired, and their hidden, often complementary or dynamic information contents. To aid the analysis of this increasing amount and complexity of medical images, medical image computing has emerged as an interdisciplinary field at the interface of computer science, engineering, physics, applied mathematics, and of course medicine. In this field, scientists aim to develop robust and accurate computational methods to extract clinically relevant information. In contrast, the field of computer vision is the science and technology of “making machines that see”, with a focus on the design, theory and implementation of techniques that allow for automatic processing and interpretation of images and videos throught the exploitation of machine learnin. Recent research in these traditionally separate fields suggests that both scientific communities could mutually benefit from one another – but a scientific gap continues to exist.
The focus of this Medical Imaging Summer School (MISS) is to train a new generation of young scientists to bridge this gap, by providing insights into the various interfaces between medical imaging, computer vision and machine learning, based on the shared broad categories of: image segmentation, registration and reconstruction, classification and modelling, and computer-aided interpretation. The course will contain a combination of in-depth tutorial-style lectures on fundamental state-of-the-art concepts, followed by accessible yet advanced research lectures using examples and applications. A broad overview of the field will be given, and guided reading groups will complement lectures. The course will be delivered by world renowned experts from both academia and industry, who are working closely at the interface of medical imaging/computer vision.
The school aims to provide a stimulating graduate training opportunity for young researchers and Ph.D. students. The participants will benefit from direct interaction with world leaders in medical image computing and computer vision (often working in both fields). Participants will also have the opportunity to present their own research, and to interact with their scientific peers, in a friendly and constructive setting.
For more information, send an email to miss@dmi.unict.it
• Roberto Cipolla, University of Cambridge, United Kingdom
• Giovanni Maria Farinella, University of Catania, Italy
• Julia Schnabel, King's College London, United Kingdom
• Filippo Stanco, University of Catania, Italy

MISS 2016 Favignana Spot 3:15
[Miss 2016] Alison Noble - Popular Classics in Machine Learning for Medical Imaging 1:05:14
[MISS 2016] Carsten Rother - Introduction to Graphical Models 1:14:48
[MISS 2016] Carsten Rother - Graphical Models in BioImedical imaging 46:52
[MISS 2016] William M. Wells III - A Graphical Introduction to Probabilistic Graphical Models 1:23:47
[MISS 2016] Ben Glocker - Random Forests and their applications in medical imaging 59:38
[MISS 2016] Nicholas Ayache - Anatomical/Physiological models in MI and biophysical simulation 1:39:05
[MISS 2016] Marleen de Bruijne - Learning imaging biomarkers: challenges and pitfalls 1:10:32
[MISS 2016] Marleen de Bruijne - Learning from Weak Labels 50:36
[MISS 2016] Ben Glocker - Solving continuous problems with discrete optimization 1:05:35
[MISS 2016] William M. Wells III - A multi-perspective introduction to the EM algorithm 31:35
[MISS 2016] William M. Wells III - Uncertainty in Registration with MCMC 45:50
[MISS 2016] Alison Noble - Learning to interpret Ultrasound Imaging 1:14:15
[MISS 2016] Daniel Rueckert - Manifold Learning, Dictionary Learning & Sparsity 57:31
[MISS 2016] Daniel Rueckert - Machine Learning for segmentation and Reconstruction 48:12
[MISS 2016] Max Welling - Approximate Bayesian Posterior Inference for Big Data 1:13:38
[MISS 2016] Raquel Urtasun - Introduction to Convolutional Neural Networks 55:07
[MISS 2016] Ben Glocker - Deep Learning for Brain Lesion Segmentation 59:57
[MISS 2016] Andrea Vedaldi - Advanced Convolutional Neural Networks 1:27:14
[MISS 2016] Raquel Urtasun - Learning Deep Structured Models 1:01:30
[MISS 2016] Andrea Vedaldi - Understanding CNNs using visualisation and transformation analysis 1:04:22
[MISS 2016] Max Welling - Deep Learning, Graphical Models and Bayesian Estimation 1:12:43
Menarini ( Miss 2016, Favignana ) 2:30

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