1.4k citations · 3.4k across the 30 of their papers we have counts for
6 papers · 1 filter
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
Kidney abnormality segmentation in thorax-abdomen CT scans
Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten +3
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalit…
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier +2
Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes…
Lumbar spine segmentation in MR images: a dataset and a public benchmark
Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens +6
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (I…
Uncertainty-Aware Multiple-Instance Learning for Reliable Classification: Application to Optical Coherence Tomography
Coen de Vente, Bram van Ginneken, Carel B. Hoyng +2
Deep learning classification models for medical image analysis often perform well on data from scanners that were used during training. However, when these models are applied to da…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…