215 citations · 227 across the 5 of their papers we have counts for
5 papers
Understanding in Artificial Intelligence
Stefan Maetschke, David Martinez Iraola, Pieter Barnard +4
Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding…
Inference of visual field test performance from OCT volumes using deep learning
Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3
Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression. Here we address the question to what degree aggregate VFT measur…
A feature agnostic approach for glaucoma detection in OCT volumes
Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCI…
nuts-flow/ml: data pre-processing for deep learning
S. Maetschke, R. Tennakoon, C. Vecchiola +1
Data preprocessing is a fundamental part of any machine learning application and frequently the most time-consuming aspect when developing a machine learning solution. Preprocessin…
Supervised, semi-supervised and unsupervised inference of gene regulatory networks
Stefan R. Maetschke, Piyush B. Madhamshettiwar, Melissa J. Davis +1
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsuper…