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Found 7 papers, 3 papers with code
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.
Partial sensitivity analysis in differential privacy
However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss.
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
$\psi$ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation.
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity.
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database.
Interpretable 2D Vision Models for 3D Medical Images
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases.
