| PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors | 0 | 0.34 | 2023 |
| Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction. | 0 | 0.34 | 2022 |
| Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images. | 0 | 0.34 | 2022 |
| Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation | 0 | 0.34 | 2022 |
| IN DEFENSE OF KALMAN FILTERING FOR POLYP TRACKING FROM COLONOSCOPY VIDEOS | 0 | 0.34 | 2022 |
| Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models | 0 | 0.34 | 2022 |
| LOW: Training deep neural networks by learning optimal sample weights | 1 | 0.35 | 2021 |
| One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization | 0 | 0.34 | 2020 |
| Special Issue: 4th Miccai Workshop On Deep Learning In Medical Image Analysis | 0 | 0.34 | 2020 |
| Self-Supervised Monocular Trained Depth Estimation Using Self-Attention And Discrete Disparity Volume | 0 | 0.34 | 2020 |
| Special Issue on Deep Learning for Robotic Vision | 0 | 0.34 | 2020 |
| Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT | 0 | 0.34 | 2020 |
| One-Stage Five-Class Polyp Detection and Classification | 0 | 0.34 | 2019 |
| A Theoretically Sound Upper Bound On The Triplet Loss For Improving The Efficiency Of Deep Distance Metric Learning | 2 | 0.36 | 2019 |
| Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. | 1 | 0.36 | 2018 |
| 1st MICCAI workshop on deep learning in medical image analysis. | 0 | 0.34 | 2018 |
| Improving the performance of pedestrian detectors using convolutional learning. | 10 | 0.50 | 2017 |
| Smart Mining for Deep Metric Learning. | 0 | 0.34 | 2017 |
| Multi-Atlas Segmentation Using Manifold Learning With Deep Belief Networks | 3 | 0.44 | 2016 |
| Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models | 30 | 1.34 | 2015 |
| Non-rigid Segmentation Using Sparse Low Dimensional Manifolds and Deep Belief Networks | 3 | 0.40 | 2014 |
| Artistic Image Analysis Using The Composition Of Human Figures | 0 | 0.34 | 2014 |
| Fuzzy Clustering Based Encoding For Visual Object Classification | 0 | 0.34 | 2013 |
| Combining A Bottom Up And Top Down Classifiers For The Segmentation Of The Left Ventricle From Cardiac Imagery | 0 | 0.34 | 2013 |
| Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds | 2 | 0.37 | 2013 |
| Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. | 28 | 1.69 | 2013 |
| The Segmentation of the Left Ventricle of the Heart From Ultrasound Data Using Deep Learning Architectures and Derivative-Based Search Methods | 36 | 1.44 | 2012 |
| In defence of RANSAC for outlier rejection in deformable registration | 27 | 0.75 | 2012 |
| Transparent and scalable terminal mobility for vehicular networks | 0 | 0.34 | 2012 |
| Artistic image classification: an analysis on the PRINTART database | 18 | 0.82 | 2012 |
| The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle segmentation in ultrasound | 2 | 0.40 | 2012 |
| Semi-supervised self-trainingmodel for the segmentationof the left ventricle of the heart from ultrasound data | 0 | 0.34 | 2011 |
| Graph-based methods for the automatic annotation and retrieval of art prints | 6 | 0.53 | 2011 |
| Fast prototyping of network protocols through ns-3 simulation model reuse | 8 | 0.84 | 2011 |
| Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images | 0 | 0.34 | 2011 |
| Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data | 4 | 0.48 | 2011 |
| Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods | 18 | 0.75 | 2010 |
| The Fusion of Deep Learning Architectures and Particle Filtering Applied to Lip Tracking | 4 | 0.39 | 2010 |
| Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures | 18 | 0.73 | 2010 |
| WiMetroNet A Scalable Wireless Network for Metropolitan Transports | 3 | 0.53 | 2010 |
| Fast and robust 3-D MRI brain structure segmentation. | 9 | 0.57 | 2009 |
| The quantitative characterization of the distinctiveness and robustness of local image descriptors | 5 | 0.43 | 2009 |
| Minimum Bayes error features for visual recognition | 0 | 0.34 | 2009 |
| FlowMonitor: a network monitoring framework for the network simulator 3 (NS-3) | 27 | 1.34 | 2009 |
| Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree | 59 | 2.69 | 2008 |
| A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. | 29 | 2.42 | 2008 |
| Integration of mobility and qos in 4g scenarios | 8 | 0.88 | 2007 |
| Supervised Learning of Semantic Classes for Image Annotation and Retrieval | 488 | 16.00 | 2007 |
| QoS abstraction layer in 4G access networks | 1 | 0.36 | 2007 |
| Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. | 8 | 1.17 | 2007 |
