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Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation
We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms …
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Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation
The Cell Tracking Challenge: 10 years of objective benchmarking
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking …
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Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation
We introduce the HueNet - a novel deep learning framework for a differentiable construction of intensity (1D) and joint (2D) histograms …
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Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos
Convolutional Neural Networks (CNNs) are considered state of the art segmentation methods for biomedical images in general and …
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Stochastic weight pruning and the role of regularization in shaping network structure
The pressing need to reduce the capacity of deep neural networks has stimulated the development of network dilution methods and their …
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An atlas of classifiers—a machine learning paradigm for brain MRI segmentation
We present the Atlas of Classifiers (AoC)—a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of …
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Subsampled brain MRI reconstruction by generative adversarial neural networks
A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing …
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Miswiring of Frontostriatal Projections in Schizophrenia
We investigated brain wiring in chronic schizophrenia and healthy controls in frontostriatal circuits using diffusion magnetic …
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Slow blood-to-brain transport underlies enduring barrier dysfunction in American football players
Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of …
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From a deep learning model back to the brain—Identifying regional predictors and their relation to aging
We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous …
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