| A deep learning approach for the analysis of masses in mammograms with minimal user intervention N Dhungel, G Carneiro, AP Bradley Medical image analysis 37, 114-128, 2017 | 387 | 2017 |
| Automated Mass Detection in Mammograms using Cascaded Deep Learning and Random Forests N Dhungel, G Carneiro, AP Bradley 2015 International Conference on Digital Image Computing: Techniques and …, 2015 | 293 | 2015 |
| Deep learning and structured prediction for the segmentation of mass in mammograms N Dhungel, G Carneiro, AP Bradley International Conference on Medical image computing and computer-assisted …, 2015 | 212 | 2015 |
| The automated learning of deep features for breast mass classification from mammograms N Dhungel, G Carneiro, AP Bradley Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 175 | 2016 |
| FULLY AUTOMATED CLASSIFICATION OF MAMMOGRAMS USING DEEP RESIDUAL NEURAL NETWORKS N Dhungel, G Carneiro, AP Bradley 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 112 | 2017 |
| Cardiac phase detection in echocardiograms with densely gated recurrent neural networks and global extrema loss FT Dezaki, Z Liao, C Luong, H Girgis, N Dhungel, AH Abdi, D Behnami, ... IEEE transactions on medical imaging 38 (8), 1821-1832, 2018 | 93 | 2018 |
| Deep Structured learning for mass segmentation from Mammograms N Dhungel, G Carneiro, AP Bradley 12th IEEE International Conference on Image Processing, ICIP, 2950--2954, 2015 | 91 | 2015 |
| Designing lightweight deep learning models for echocardiography view classification H Vaseli, Z Liao, AH Abdi, H Girgis, D Behnami, C Luong, FT Dezaki, ... Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and …, 2019 | 40 | 2019 |
| Deep residual recurrent neural networks for characterisation of cardiac cycle phase from echocardiograms FT Dezaki, N Dhungel, AH Abdi, C Luong, T Tsang, J Jue, K Gin, ... Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 39 | 2017 |
| TREE RE-WEIGHTED BELIEF PROPAGATION USING DEEP LEARNING POTENTIALS FOR MASS SEGMENTATION FROM MAMMOGRAMS N Dhungel, G Carneiro, AP Bradley 12th IEEE International Symposium on Biomedical Imaging, ISBI, 760-763, 2015 | 39 | 2015 |
| Multi-scale mass segmentation for mammograms via cascaded random forests H Min, SS Chandra, N Dhungel, S Crozier, AP Bradley 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 21 | 2017 |
| Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods A Nana, JMD Staynor, S Arlai, A El-Sallam, N Dhungel, MK Smith Obesity Research & Clinical Practice 16 (1), 37-43, 2022 | 15 | 2022 |
| Mass segmentation in mammograms: A cross-sensor comparison of deep and tailored features JS Cardoso, N Marques, N Dhungel, G Carneiro, AP Bradley 2017 IEEE International Conference on Image Processing (ICIP), 1737-1741, 2017 | 15 | 2017 |
| Automated detection of individual micro-calcifications from mammograms using a multi-stage cascade approach Z Lu, G Carneiro, N Dhungel, AP Bradley arXiv preprint arXiv:1610.02251, 2016 | 15 | 2016 |
| Combining deep learning and structured prediction for segmenting masses in mammograms N Dhungel, G Carneiro, AP Bradley Deep Learning and Convolutional Neural Networks for Medical Image Computing …, 2017 | 8 | 2017 |
| Automated detection, segmentation and classification of masses from mammograms using deep learning N Dhungel | 1 | 2016 |
| A Deep Learning approach to fully automated analysis of Masses in Mammograms N Dhungel, G Carneiro, AP Bradley Automated Detection, Segmentation and Classification of Masses from …, 2016 | | 2016 |
| A New QRS Detection Algorithm Based on Combined Fuzzy Logic and Wavelet Technique E Timoshenko, N Dhungel 5th European Conference of the International Federation for Medical and …, 2012 | | 2012 |
| Three-dimensional localization of brain bioelectric activity in gambling addiction and epilepsy N Dhungel, E Timoshenko | | |
Andrew P. BradleyWhipbird SignalsVerified email at ieee.org
