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Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory.
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation.
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