107 citations
- Imperial College LondonGB7 papers
- NIHR Imperial Biomedical Research CentreGB3 papers
- Technical University of MunichDE2 papers
- TUM KlinikumDE2 papers
- Charles UniversityCZ1 paper
- Consejo Nacional de Investigaciones Científicas y TécnicasAR1 paper
- Erasmus MCNL1 paper
- Erasmus University RotterdamNL1 paper
- Friedrich-Alexander-Universität Erlangen-NürnbergDE1 paper
- Graz University of TechnologyAT1 paper
- Indian Institute of Technology MadrasIN1 paper
- Institute of Mathematical StatisticsUS1 paper
5 papers · 1 filter
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Philip Müller, Felix Meissen, Georgios Kaissis +1
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successe…
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis +1
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the n…
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Tianrui Liu, Qingjie Meng, Jun-Jie Huang +3
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content…
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Agostina J Larrazabal, César Martínez, Ben Glocker +1
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.…
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Maximilian Seitzer, Guang Yang, Jo Schlemper +9
Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE)…