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Found 5 papers, 5 papers with code
Learning Disease State from Noisy Ordinal Disease Progression Labels
Learning from noisy ordinal labels is a key challenge in medical imaging.
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging
Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more.
Disentangling representations of retinal images with generative models
Through qualitative and quantitative analyses, we show that our models encode desired information in disentangled subspaces and enable controllable image generation based on the learned subspaces, demonstrating the effectiveness of our disentanglement loss.
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
Local policy search with Bayesian optimization
We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
