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phd candidate 'deep learning medical imaging pancreas'

phd candidate 'deep learning medical imaging pancreas'

Netherlands 31 Dec 2020
Radboud University Medical Center Radboudumc

Radboud University Medical Center Radboudumc

State University, Browse similar opportunities

OPPORTUNITY DETAILS

Total reward
0 $
State University
Area
Host Country
Deadline
31 Dec 2020
Study level
Opportunity type
PhD
Specialities
Opportunity funding
Full funding
Eligible Countries
This opportunity is destined for all countries
Eligible Region
All Regions

For the project 'Deep learning medical imaging pancreas' we are seeking for 3 PhD candidates.

Pancreatic cancer (PDAC) will soon become the second leading cause of cancer-related death in Western societies, worldwide half a million deaths per year. PDAC has the lowest survival of all cancers (median survival time 4.6 months, with patients losing 98% of their healthy life expectancy). The biggest challenge in the management of PDAC and new treatment development is the current inability of patient stratification. PDAC research in genomics, pathomics, and radiomics is scattered and limited, and it is not impacting PDAC healthcare.

Genomics technologies such as next-generation whole-genome sequencing (WGS) and liquid biopsy (ctDNA) are emerging, but not yet routinely used in PDAC diagnosis. Major R&D investments in prostate, lung, and breast cancer have resulted in stabilizing or decreasing mortality. No evidence-based personalized medicine approach for PDAC is currently available in the clinical practice.

Artificial Intelligence (AI) is transforming the field of healthcare. Worldwide interest in artificial intelligence (AI) is high and snowballing, fuelled by the availability of large datasets ("big data"). A new EU project (PANCAIM) aims to pool PDAC omics research data with medical imaging using AI to make an impact on PDAC patients and healthcare.

In Radboudumc PANCAIM aims to research deep learning on PDAC medical imaging (CT,MR,Pathology). Radboudumc is a clinical expert on PDAC and technical expert in the field of deep learning/AI in medical imaging for over 20 years.


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