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20162023
most citedAttention U-Net: Learning Where to Look for the Pancreas

4.6k citations · 14.9k across the 210 of their papers we have counts for

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Showing 2020Show all

28 papers · 1 filter

cs.CR2020★ 7 cited

Privacy-preserving medical image analysis

Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel +8

The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy pr…

cs.LG2020★ 10 cited

2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach

Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, d…

cs.CR2020★ 11 cited

A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach

This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification. The in-depth analysis of these…

eess.IV2020

Reducing Textural Bias Improves Robustness of Deep Segmentation Models

Seoin Chai, Daniel Rueckert, Ahmed E. Fetit

Despite advances in deep learning, robustness under domain shift remains a major bottleneck in medical imaging settings. Findings on natural images suggest that deep neural models…

cs.CV2020

Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer +4

Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form…

cs.CV2020

Patch-based Brain Age Estimation from MR Images

Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson +2

Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. This is a potential biomarker…