44 citations
- University College LondonGB4 papers
- King's College LondonGB3 papers
- KU LeuvenBE3 papers
- London Women's ClinicGB2 papers
- Medical University of ViennaAT2 papers
- Amsterdam University Medical CentersNL1 paper
- Centre for Eye Research AustraliaAU1 paper
- Goethe University FrankfurtDE1 paper
- Institute for Reproductive HealthUS1 paper
- King's College SchoolGB1 paper
- Moorfields Eye HospitalGB1 paper
- Moorfields Eye Hospital NHS Foundation TrustGB1 paper
4 papers
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Roy Schwartz, Hagar Khalid, Sandra Liakopoulos +12
Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography sca…
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Lucas Fidon, Michael Aertsen, Florian Kofler +18
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, wi…
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
Lucas Fidon, Michael Aertsen, Nada Mufti +13
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Lucas Fidon, Michael Aertsen, Doaa Emam +9
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods…