Join us as we set up six exciting use cases and prove that radiologists can get involved in shaping the AI tools of the future…
T E A S E R
WATCH EPISODE 1
E P I S O D E S
January 22, 2020 | 19:30 CET
The one where we tackle fibrosisHumans cannot identify complex patterns in CT data reliably. In the detection of fibrosis, relevant patterns need to be identified and we suspect that we don’t know all of them. We will write an algorithm that detects the relevant type of fibrosis and explain the difference between supervised and unsupervised learning.
January 29, 2020 | 19:30 CET
The one with the six packWhen assessing sarcopenia, the L3 level has to be detected manually on the whole body CT, the muscle bulk has to be segmented and corrections of these segmentations to remove adipose tissue have to be made manually.
February 5, 2020 | 19:30 CET
The one that might be renal cancerWe propose to write an algorithm that will automatically detect and segment renal masses on CT. The segmented mass will then be further characterised using a radiomic approach, to differentiate benign from malignant masses.
February 12, 2020 | 19:30 CET
The one where we make the connectionCorpus callosum agenesis (CCA) is one of the most common brain malformations. Fetal MRI improves identification of parenchymal anomalies in CCA in relation to US. When isolated, CCA has a good prognosis in 70-80% of children.
February 19, 2020 | 19:30 CET
The one with whole body MRIWe propose to write an algorithm that will automatically detect malignant lesions on whole body MRI.
S P E A K E R S