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Found 8 papers, 0 papers with code
Application of Homomorphic Encryption in Medical Imaging
In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust model of data governance.
Precision-Weighted Federated Learning
Our performance evaluations show 9% better predictions with MNIST, 18% with Fashion-MNIST, and 5% with CIFAR-10 in the non-IID setting.
Cross-Modal Information Maximization for Medical Imaging: CMIM
In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as the different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.)
Learn Faster and Forget Slower via Fast and Stable Task Adaptation
We empirically show that compared to prevailing fine-tuning practices, FAST learns the target task faster and forgets the source task slower.
Dual Adversarial Inference for Text-to-Image Synthesis
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
Efficient Neural Task Adaptation by Maximum Entropy Initialization
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples.
InfoMask: Masked Variational Latent Representation to Localize Chest Disease
The scarcity of richly annotated medical images is limiting supervised deep learning based solutions to medical image analysis tasks, such as localizing discriminatory radiomic disease signatures.
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
