1.2k citations
- Technical University of MunichDE99 papers
- Imperial College LondonGB36 papers
- Helmholtz MunichDE21 papers
- University of ZurichCH16 papers
- Ludwig-Maximilians-Universität MünchenDE12 papers
- Johns Hopkins UniversityUS11 papers
- Harvard UniversityUS10 papers
- Heidelberg UniversityDE10 papers
- Radboud University Medical CenterNL10 papers
- Radboud University NijmegenNL10 papers
- Deutsches Herzzentrum MünchenDE9 papers
- King's College LondonGB9 papers
13 papers · 1 filter
Joint Learning of Localized Representations from Medical Images and Reports
Philip Müller, Georgios Kaissis, Congyu Zou +1
Contrastive learning has proven effective for pre-training image models on unlabeled data with promising results for tasks such as medical image classification. Using paired text (…
Estimating Total Lung Volume from Pixel-level Thickness Maps of Chest Radiographs Using Deep Learning
Tina Dorosti, Manuel Schultheiss, Philipp Schmette +11
Purpose: To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learni…
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
Turkay Kart, Wenjia Bai, Ben Glocker +1
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due…
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
Felix Meissen, Georgios Kaissis, Daniel Rueckert
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pa…
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
Chen Chen, Kerstin Hammernik, Cheng Ouyang +3
Deep learning-based segmentation methods are vulnerable to unforeseen data distribution shifts during deployment, e.g. change of image appearances or contrasts caused by different…
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…