312 citations · 613 across the 13 of their papers we have counts for
5 papers · 1 filter
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particu…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have…
Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander +2
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the la…
Autofocus Layer for Semantic Segmentation
Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha +4
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus lay…