Search Results for author: Antonio Criminisi

Found 20 papers, 4 papers with code

Deep Learning with Mixed Supervision for Brain Tumor Segmentation

no code implementations10 Dec 2018 Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache

In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.

Brain Tumor Segmentation General Classification +1

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

no code implementations23 Jul 2018 Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache

Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.

Tumor Segmentation

Adaptive Neural Trees

1 code implementation ICLR 2019 Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.

General Classification Representation Learning

Semi-Supervised Learning via Compact Latent Space Clustering

no code implementations ICML 2018 Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.

Autofocus Layer for Semantic Segmentation

3 code implementations22 May 2018 Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison Cottrell, Antonio Criminisi, Aditya Nori

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

Brain Tumor Segmentation Tumor Segmentation

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

no code implementations28 Dec 2016 Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.

Lesion Segmentation Unsupervised Domain Adaptation

Measuring Neural Net Robustness with Constraints

1 code implementation NeurIPS 2016 Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled.

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

no code implementations CVPR 2017 Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.

Refining Architectures of Deep Convolutional Neural Networks

no code implementations CVPR 2016 Sukrit Shankar, Duncan Robertson, Yani Ioannou, Antonio Criminisi, Roberto Cipolla

Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks.

Decision Forests, Convolutional Networks and the Models in-Between

1 code implementation3 Mar 2016 Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi

We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.

Image Classification Representation Learning

Deep Neural Decision Forests

no code implementations ICCV 2015 Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo

We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.

Global Optimization Representation Learning

Training CNNs with Low-Rank Filters for Efficient Image Classification

no code implementations20 Nov 2015 Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi

Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.

Classification General Classification +1

Filter Forests for Learning Data-Dependent Convolutional Kernels

no code implementations CVPR 2014 Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.

Denoising

Decision Jungles: Compact and Rich Models for Classification

no code implementations NeurIPS 2013 Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi

Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.

Classification General Classification

GeoF: Geodesic Forests for Learning Coupled Predictors

no code implementations CVPR 2013 Peter Kontschieder, Pushmeet Kohli, Jamie Shotton, Antonio Criminisi

This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.

Semantic Segmentation Structured Prediction

Context-Sensitive Decision Forests for Object Detection

no code implementations NeurIPS 2012 Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof

In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem.

General Classification Object Detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.