Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.

Background: Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training.

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

Background: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC).

Patients And Methods: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers.

Privacy: An Axiomatic Approach.

The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows.

Using Cumulative Impact Assessment as a Smokescreen in New South Wales Alcohol Harm Reduction Laws: A Commentary.

In 2020 the New South Wales Liquor Act was amended to allow the Independent Liquor and Gaming Authority (ILGA) to approve new liquor authorisations in parts of the Sydney CBD otherwise subject to a freeze. The vehicle for this was called Cumulative Impact Assessment (CIA). The Amendment added promotion of business vitality to an established list of considerations previously set out by ILGA in its Guideline (6) on social impact assessment.

Medical imaging deep learning with differential privacy.

The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm.