167 citations · 252 across the 9 of their papers we have counts for
9 papers
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
Erfan Darzi, Yiqing Shen, Yangming Ou +2
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the…
The Hidden Adversarial Vulnerabilities of Medical Federated Learning
Erfan Darzi, Florian Dubost, Nanna. M. Sijtsema +1
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradien…
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks
Erfan Darzi, Nanna M. Sijtsema, P. M. A van Ooijen
This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggrega…
Exploring adversarial attacks in federated learning for medical imaging
Erfan Darzi, Florian Dubost, N. M. Sijtsema +1
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities o…
Masked conditional variational autoencoders for chromosome straightening
Jingxiong Li, Sunyi Zheng, Zhongyi Shui +9
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists…
A Comparative Study of Federated Learning Models for COVID-19 Detection
Erfan Darzidehkalani, Nanna M. Sijtsema, P. M. A van Ooijen
Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no ac…
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images
Alessia De Biase, Nanna Maria Sijtsema, Lisanne van Dijk +2
Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC),…
Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification
Sunyi Zheng, Ludo J. Cornelissen, Xiaonan Cui +4
Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung no…
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
Sunyi Zheng, Jiapan Guo, Xiaonan Cui +3
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule d…