Data Sciences and Brain Health across the Life Course session in 2020 SICSA Conference Oct 01, 2020 01:15 PM - 03:15 PM — Virtual Meeting (online)
Ophthalmic Medical Image Analysis MICCAI 2020 Workshop Oct 08, 2020 12:00 AM — Virtual Meeting (online)
Predictive Intelligence in Medicine MICCAI 2020 Workshop Oct 08, 2020 12:00 AM — Virtual Meeting (online)
PET is Wonderful Annual Meeting 2020 Oct 27, 2020 02:00 PM - 05:40 PM — Virtual Meeting (online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

Predictive Intelligence in Medicine MICCAI 2020 Workshop

3rd Workshop on PRedictive Intelligence in MEdicine (PRIME) at MICCAI 2020
When Oct 08, 2020
from 12:00 AM to 12:00 AM
Where Virtual Meeting (online)
Contact Name
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UPDATE: this event is being moved to a virtual format due to spread of the COVID-19 Coronavirus.

Big and complex data is fuelling diverse research directions in both medical image analysis and computer vision research fields. These can be divided into two main categories: (1) analytical methods, and (2) predictive methods. While analytical methods aim to efficiently analyse, represent and interpret data (static or longitudinal), predictive methods leverage the data currently available to predict observations at later time-points (i.e., forecasting the future) or predicting observations at earlier time-points (i.e., predicting the past for missing data completion). For instance, a method which only focuses on classifying patients with mild cognitive impairment (MCI) and patients with Alzheimer’s disease (AD) is an analytical method, while a method which predicts if a subject diagnosed with MCI will remain stable or convert to AD over time is a predictive method. Similar examples can be established for various neurodegenerative or neuropsychiatric disorders, degenerative arthritis or in cancer studies, in which the disease/disorder develops over time.

It would constitute a stunning progress in the Medical Image Computing and Computer Assisted Intervention (MICCAI) research community if, in a few years, we contribute to engineering a ‘predictive intelligence’ which can map both low-dimensional and high-dimensional medical data onto the future with high precision. This workshop is the first endeavor to drive the field of ‘high-precision predictive medicine’, where late medical observations are predicted with high precision, while providing explanation via machine and deep learning, and statistically, mathematically- or physically-based models of healthy, disordered development and ageing. Despite the terrific progress that analytical methods have made in the last twenty years in medical image segmentation, registration or other related applications, efficient predictive intelligent models/methods are somewhat lagging behind. As such predictive intelligence develops and improves —and this is likely to do so exponentially in the coming years— this will have far-reaching consequences for the development of new treatment procedures and novel technologies. These predictive models will begin to shed light on one of the most complex healthcare and medical challenges we have ever encountered, and, in doing so, change our basic understanding of who we are.

This workshop will mediate ideas from both machine learning and mathematical/statistical/physical modeling research directions in the hope to provide a deeper understanding of the foundations of predictive intelligence developed for medicine, as well as to where we currently stand and what we aspire to achieve through this field. PRIME-MICCAI 2020 will feature a single-track workshop with keynote speakers with deep expertise in high-precision predictive medicine using machine learning and other modeling approaches —which are believed to stand at opposing directions. Our workshop will also include technical paper presentations, poster sessions, and demonstrations. Eventually, this will help steer a wide spectrum of MICCAI publications from being ‘only analytical’ to being ‘jointly analytical and predictive’.

Call for papers (submission deadline: 23 June 2020)

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