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Found 3 papers, 1 papers with code
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
Generating Unrestricted Adversarial Examples via Three Parameters
It also reduces the model accuracy by an average of 73% on six datasets MNIST, FMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet.
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.
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