AIMC Topic: Privacy
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Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.
Computer methods and programs in biomedicine
Apr 19, 2025
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys ...
Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.
Scientific reports
Apr 16, 2025
In the digital age, privacy preservation is of paramount importance while processing health-related sensitive information. This paper explores the integration of Federated Learning (FL) and Differential Privacy (DP) for breast cancer detection, lever...
Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.
Scientific reports
Apr 11, 2025
Ensuring data privacy in medical image classification is a critical challenge in healthcare, especially with the increasing reliance on AI-driven diagnostics. In fact, over 30% of healthcare organizations globally have experienced a data breach in th...
A study on innovation resistance of artificial intelligence voice assistants based on privacy infringement and risk perception.
PloS one
Apr 8, 2025
As a vital tool for human-computer interaction, artificial intelligence (AI) voice assistants have become an integral part of individuals' everyday routines. However, there are still a series of problems caused by privacy violations in current use. T...
DEeR: Deviation Eliminating and Noise Regulating for Privacy-Preserving Federated Low-Rank Adaptation.
IEEE transactions on medical imaging
Apr 3, 2025
Integrating low-rank adaptation (LoRA) with federated learning (FL) has received widespread attention recently, aiming to adapt pretrained foundation models (FMs) to downstream medical tasks via privacy-preserving decentralized training. However, owi...
Model interpretability on private-safe oriented student dropout prediction.
PloS one
Mar 31, 2025
Student dropout is a significant social issue with extensive implications for individuals and society, including reduced employability and economic downturns, which, in turn, drastically influence social sustainable development. Identifying students ...
Retinal imaging in an era of open science and privacy protection.
Experimental eye research
Mar 14, 2025
Artificial intelligence (AI) holds great promise for analyzing complex data to advance patient care and disease research. For example, AI interpretation of retinal imaging may enable the development of noninvasive retinal biomarkers of systemic disea...
A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.
Scientific reports
Mar 1, 2025
The traditional molecular-based identification (TMID) technique of new infections from genome sequences (GSs) has made significant contributions so far. However, due to the sensitive nature of the medical data, the TMID technique of transferring the ...
A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.
PloS one
Feb 11, 2025
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...
Is more data always better? On alternative policies to mitigate bias in Artificial Intelligence health systems.
Bioethics
Feb 9, 2025
The development and implementation of Artificial Intelligence (AI) health systems represent a great power that comes with great responsibility. Their capacity to improve and transform healthcare involves inevitable risks. A major risk in this regard ...
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