4.6k citations · 14.9k across the 210 of their papers we have counts for
42 papers · 1 filter
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit +7
Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g.,…
Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging
Wenqi Huang, Hongwei Li, Jiazhen Pan +3
In this work, we propose a novel image reconstruction framework that directly learns a neural implicit representation in k-space for ECG-triggered non-Cartesian Cardiac Magnetic Re…
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning
Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert +1
Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially priva…
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images
Philip Müller, Georgios Kaissis, Daniel Rueckert
Image-text contrastive learning has proven effective for pretraining medical image models. When targeting localized downstream tasks like semantic segmentation or object detection,…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
Linus Kreitner, Ivan Ezhov, Daniel Rueckert +2
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a n…