131 citations · 146 across the 5 of their papers we have counts for
5 papers
Deep Generative Classification of Blood Cell Morphology
Simon Deltadahl, Julian Gilbey, Christine Van Laer +16
Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of ce…
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
Daniel Kreuter, Samuel Tull, Julian Gilbey +8
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of ma…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images
Julian D. Gilbey, Carola-Bibiane Schönlieb
Some historical and more recent printed documents have been scanned or stored at very low resolutions, such as 60 dpi. Though such scans are relatively easy for humans to read, the…