139 citations · 288 across the 4 of their papers we have counts for
7 papers · 1 filter
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat +3
Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns.…
Needles in Haystacks: On Classifying Tiny Objects in Large Images
Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas +3
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional N…
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Xiaoran Chen, Nick Pawlowski, Martin Rajchl +2
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributio…
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
Martin Rajchl, Nick Pawlowski, Daniel Rueckert +2
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM. The network is trained on 5…
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee +4
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its…