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of 298
pro vyhledávání: '"van Ooijen, P M A"'
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 gradient information from prior global model updates, adversaries can enhance
Externí odkaz:
http://arxiv.org/abs/2310.13893
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the performance of underrepresented clients. However, these methods often
Externí odkaz:
http://arxiv.org/abs/2310.09444
This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The c
Externí odkaz:
http://arxiv.org/abs/2310.08681
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 of federated learning networks in medical image analysis against such a
Externí odkaz:
http://arxiv.org/abs/2310.06227
Autor:
Li, Jingxiong, Zheng, Sunyi, Shui, Zhongyi, Zhang, Shichuan, Yang, Linyi, Sun, Yuxuan, Zhang, Yunlong, Li, Honglin, Ye, Yuanxin, van Ooijen, Peter M. A., Li, Kang, Yang, Lin
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists from analyzing chromosome types. To address this issue, we propose a
Externí odkaz:
http://arxiv.org/abs/2306.14129
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 access to data from other hospitals. Federated learning (FL) has been us
Externí odkaz:
http://arxiv.org/abs/2303.16141
Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access t
Externí odkaz:
http://arxiv.org/abs/2201.08356
Autor:
Qiu, Bingjiang, Guo, Jiapan, Kraeima, Joep, Glas, Haye H., Borra, Ronald J. H., Witjes, Max J. H., van Ooijen, Peter M. A.
Recently, accurate mandible segmentation in CT scans based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, metal artifacts among mandibles and large variations in shape or size among ind
Externí odkaz:
http://arxiv.org/abs/2003.06486
Autor:
Zheng, Sunyi, Cornelissen, Ludo J., Cui, Xiaonan, Jing, Xueping, Veldhuis, Raymond N. J., Oudkerk, Matthijs, van Ooijen, Peter M. A.
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 nodules while keeping good performance for large nodules. Methods: We pr
Externí odkaz:
http://arxiv.org/abs/2001.04537
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