3 papers
cs.CV2018
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Lorenz Berger, Eoin Hyde, Matt Gibb +4
Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of perfor…
cs.CV2018
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
Luis C. Garcia-Peraza-Herrera, Martin Everson, Wenqi Li +10
In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification o…
cs.CV2017
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Lorenz Berger, Eoin Hyde, M. Jorge Cardoso +1
Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, tra…