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Found 7 papers, 2 papers with code
Perplexity-free Parametric t-SNE
The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method.
Deep Learning to Detect Bacterial Colonies for the Production of Vaccines
During the development of vaccines, bacterial colony forming units (CFUs) are counted in order to quantify the yield in the fermentation process.
Knowing what you know in brain segmentation using Bayesian deep neural networks
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks
The generated CT images have good global and local features of a real CT image and can be used to augment the training datasets for effective learning.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Collecting the large datasets needed to train deep neural networks can be very difficult, particularly for the many applications for which sharing and pooling data is complicated by practical, ethical, or legal concerns.
Blind Deconvolution of PET Images using Anatomical Priors
Images from positron emission tomography (PET) provide metabolic information about the human body.
Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy.
