The Multiscale Classifier. - Brian C. Lovell; Andrew P. Bradley (IEEE Trans. Pattern Anal. Mach. Intell., 1996)

ROC curves and the X2 test. - Andrew P. Bradley (Pattern Recognit. Lett., 1996)

Machine learning for medical diagnostics: techniques for feature extraction, classification, and evaluation - Andrew P. Bradley (1996)

The use of the area under the ROC curve in the evaluation of machine learning algorithms. - Andrew P. Bradley (Pattern Recognit., 1997)

Perceptual quality metrics applied to still image compression. - Michael P. Eckert; Andrew P. Bradley (Signal Process., 1998)

A wavelet visible difference predictor. - Andrew P. Bradley (IEEE Trans. Image Process., 1999)

Shift-invariance in the Discrete Wavelet Transform. - Andrew P. Bradley (DICTA, 2003)

Visual attention for region of interest coding in JPEG 2000. - Andrew P. Bradley; Fred Stentiford (J. Vis. Commun. Image Represent., 2003)

Sample Size Estimation using the Receiver Operating Characteristic Curve. - Andrew P. Bradley; I. Dennis Longstaff (ICPR (4), 2004)

Towards Optimal Image Stitching for Virtual Microscopy. - Ben Appleton; Andrew P. Bradley; Michael Wildermoth (DICTA, 2005)

Virtual Microscopy with Extended Depth of Field. - Andrew P. Bradley; Michael Wildermoth; Paul Mills (DICTA, 2005)

Estimation Of Epipolar Geometry Via The Radon Transform. - Stefan Lehmann; Andrew P. Bradley; I. Vaughan L. Clarkson (ICASSP (2), 2006)

Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve. - Nahla H. Barakat; Andrew P. Bradley (ICPR (2), 2006)

Automatic Segmentation of Enhancing Breast Tissue in Dynamic Contrast-Enhanced MR Images. - Yaniv Gal; Andrew Mehnert; Andrew P. Bradley; Kerry McMahon; Stuart Crozier (DICTA, 2007)

A Comparison of DCT and DWT Block Based Watermarking on Medical Image Quality. - Jason Dowling; Birgit M. Planitz; Anthony J. Maeder; Jiang Du 002; Binh Pham 001; Colin Boyd; Shaokang Chen; Andrew P. Bradley; Stuart Crozier (IWDW, 2007)

Correspondence-Free Determination of the Affine Fundamental Matrix. - Stefan Lehmann; Andrew P. Bradley; I. Vaughan L. Clarkson; John Williams 004; Peter J. Kootsookos (IEEE Trans. Pattern Anal. Mach. Intell., 2007)

Rule Extraction from Support Vector Machines: A Sequential Covering Approach. - Nahla H. Barakat; Andrew P. Bradley (IEEE Trans. Knowl. Data Eng., 2007)

Mixed content image compression by gradient field integration. - Peter W. M. Ilbery; David Taubman; Andrew P. Bradley (ICIP, 2008)

Group-based meta-classification. - Noor Azah Samsudin; Andrew P. Bradley (ICPR, 2008)

Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast. - Yaniv Gal; Andrew Mehnert; Andrew P. Bradley; Dominic Kennedy; Stuart Crozier (DICTA, 2009)

The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines. - Nahla H. Barakat; Andrew P. Bradley (MLDM, 2009)

On the Estimation of Extrinsic and Intrinsic Parameters of Optical Microscope Calibration. - Doreen Altinay; Andrew P. Bradley; Andrew Mehnert (DICTA, 2010)

Rule extraction from support vector machines: A review. - Nahla H. Barakat; Andrew P. Bradley (Neurocomputing, 2010)

Nearest neighbour group-based classification. - Noor Azah Samsudin; Andrew P. Bradley (Pattern Recognit., 2010)

Intelligible support vector machines for diagnosis of diabetes mellitus. - Nahla H. Barakat; Andrew P. Bradley; Mohamed Nabil H. Barakat (IEEE Trans. Inf. Technol. Biomed., 2010)

Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means. - Yaniv Gal; Andrew Mehnert; Andrew P. Bradley; Kerry McMahon; Dominic Kennedy; Stuart Crozier (IEEE Trans. Medical Imaging, 2010)

Investigation of the non-Markovity Spectrum as a Cognitive Processing Measure of Deep Brain Microelectrode Recordings. - P. A. Meehan; P. A. Bellette; Andrew P. Bradley; J. E. Castner; Helen J. Chenery; David A. Copland; John J. Varghese; Terry Coyne; P. A. Silburn (BIOSIGNALS, 2011)

An Evaluation of Multi-resolution Microscope Slide Scanning Algorithms. - Doreen Altinay; Andrew P. Bradley (DICTA, 2011)

Effect of competing stimuli on SSVEP-based BCI. - Kian B. Ng; Andrew P. Bradley; Ross Cunnington (EMBC, 2011)

A Comparison of Multiple Instance and Group Based Learning. - Steven D. Brossi; Andrew P. Bradley (DICTA, 2012)

Enhancing the classification accuracy of Steady-State Visual Evoked Potential-based Brain-Computer Interface using Component Synchrony Measure. - Kian B. Ng; Ross Cunnington; Andrew P. Bradley (IJCNN, 2012)

Illumination Effects in Quantitative Virtual Microscopy. - Doreen Altinay; Andrew P. Bradley (CAIP (2), 2013)

Performance Analysis of Three Microscope Slide Scanning Techniques. - Yilun Fan; Yaniv Gal; Andrew P. Bradley (DICTA, 2013)

Effect of posterized naturalistic stimuli on SSVEP-based BCI. - Kian B. Ng; Andrew P. Bradley; Ross Cunnington (EMBC, 2013)

Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells. - Zhi Lu; Gustavo Carneiro 001; Andrew P. Bradley (MICCAI (1), 2013)

Evaluation of a morphological filter in mean cardiac output determination: application to left ventricular assist devices. - Michael Charles Stevens; Andrew P. Bradley; Stephen J. Wilson; David G. Mason (Medical Biol. Eng. Comput., 2013)

ROC curve equivalence using the Kolmogorov-Smirnov test. - Andrew P. Bradley (Pattern Recognit. Lett., 2013)

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging. - Fahira A. Maken; Yaniv Gal; Darryl McClymont; Andrew P. Bradley (DICTA, 2014)

Characterization of movements during restless sleep in children: A pilot study. - Marnie L. Lamprecht; Philip Ian Terrill; Chloe L. Parsley; Andrew P. Bradley (EMBC, 2014)

Microscopic specimen delineation using auto-phase correlation index. - Yilun Fan; Yaniv Gal; Andrew P. Bradley (ISBI, 2014)

Extended Naïve Bayes for Group Based Classification. - Noor Azah Samsudin; Andrew P. Bradley (SCDM, 2014)

Half-AUC for the evaluation of sensitive or specific classifiers. - Andrew P. Bradley (Pattern Recognit. Lett., 2014)

Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (DICTA, 2015)

Anatomical Skin Segmentation in Reflectance Confocal Microscopy with Weak Labels. - Samuel C. Hames; Marco Ardigò; H. Peter Soyer; Andrew P. Bradley; Tarl W. Prow (DICTA, 2015)

Expectation-Maximization with Image-Weighted Markov Random Fields to Handle Severe Pathology. - Alex M. Pagnozzi; Nicholas D. H. Dowson; Andrew P. Bradley; Roslyn N. Boyd; Pierrick Bourgeat; Stephen E. Rose (DICTA, 2015)

Multiple instance learning for breast MRI based on generic spatio-temporal features. - Fahira Afzal Maken; Andrew P. Bradley (ICASSP, 2015)

Spectral properties of neuronal pulse interval modulation. - John J. Varghese; Kristian J. Weegink; P. A. Bellette; Andrew P. Bradley (ICASSP, 2015)

Spikes from compound action potentials in simulated microelectrode recordings. - Kristian J. Weegink; John J. Varghese; Andrew P. Bradley (ICASSP, 2015)

Deep structured learning for mass segmentation from mammograms. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (ICIP, 2015)

Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (ISBI, 2015)

Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models. - Gustavo Carneiro 001; Jacinto C. Nascimento; Andrew P. Bradley (MICCAI (3), 2015)

Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (MICCAI (1), 2015)

Segmentation of skin strata in reflectance confocal microscopy depth stacks. - Samuel C. Hames; Marco Ardigò; H. Peter Soyer; Andrew P. Bradley; Tarl W. Prow (Image Processing, 2015)

An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells. - Zhi Lu; Gustavo Carneiro 001; Andrew P. Bradley (IEEE Trans. Image Process., 2015)

The Automated Learning of Deep Features for Breast Mass Classification from Mammograms. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (MICCAI (2), 2016)

Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach. - Zhi Lu; Gustavo Carneiro 001; Neeraj Dhungel; Andrew P. Bradley (CoRR, 2016)

Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions1. - Gustavo Carneiro 001; Jacinto C. Nascimento; Andrew P. Bradley (Deep Learning for Medical Image Analysis, 2017)

Mass segmentation in mammograms: A cross-sensor comparison of deep and tailored features. - Jaime S. Cardoso 001; Nuno Marques; Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (ICIP, 2017)

Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. - Gustavo Carneiro 001; Luke Oakden-Rayner; Andrew P. Bradley; Jacinto C. Nascimento; Lyle J. Palmer (ISBI, 2017)

Fully automated classification of mammograms using deep residual neural networks. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (ISBI, 2017)

Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. - Gabriel Maicas; Gustavo Carneiro 001; Andrew P. Bradley (ISBI, 2017)

Multi-scale mass segmentation for mammograms via cascaded random forests. - Hang Min; Shekhar S. Chandra; Neeraj Dhungel; Stuart Crozier; Andrew P. Bradley (ISBI, 2017)

Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI. - Gabriel Maicas; Gustavo Carneiro 001; Andrew P. Bradley; Jacinto C. Nascimento; Ian D. Reid 001 (MICCAI (3), 2017)

Detecting hip fractures with radiologist-level performance using deep neural networks. - William Gale; Luke Oakden-Rayner; Gustavo Carneiro 001; Andrew P. Bradley; Lyle J. Palmer (CoRR, 2017)

A deep learning approach for the analysis of masses in mammograms with minimal user intervention. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (Medical Image Anal., 2017)

Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells. - Zhi Lu; Gustavo Carneiro 001; Andrew P. Bradley; Daniela Ushizima; Masoud S. Nosrati; Andrea G. C. Bianchi; Cláudia M. Carneiro; Ghassan Hamarneh (IEEE J. Biomed. Health Informatics, 2017)

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning. - Gustavo Carneiro 001; Jacinto C. Nascimento; Andrew P. Bradley (IEEE Trans. Medical Imaging, 2017)

Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms. - Neeraj Dhungel; Gustavo Carneiro 001; Andrew P. Bradley (Deep Learning and Convolutional Neural Networks for Medical Image Computing, 2017)

Training Medical Image Analysis Systems like Radiologists. - Gabriel Maicas; Andrew P. Bradley; Jacinto C. Nascimento; Ian D. Reid 001; Gustavo Carneiro 001 (MICCAI (1), 2018)

How to Exploit Weaknesses in Biomedical Challenge Design and Organization. - Annika Reinke; Matthias Eisenmann; Sinan Onogur; Marko Stankovic 002; Patrick Scholz 001; Peter M. Full; Hrvoje Bogunovic; Bennett A. Landman; Oskar Maier; Bjoern H. Menze; Gregory C. Sharp; Korsuk Sirinukunwattana; et al. (MICCAI (4), 2018)

1st MICCAI workshop on deep learning in medical image analysis. - Gustavo Carneiro 001; João Manuel R. S. Tavares; Andrew P. Bradley; João Paulo Papa; Jacinto C. Nascimento; Jaime S. Cardoso 001; Zhi Lu; Vasileios Belagiannis (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2018)

Producing radiologist-quality reports for interpretable artificial intelligence. - William Gale; Luke Oakden-Rayner; Gustavo Carneiro 001; Andrew P. Bradley; Lyle J. Palmer (CoRR, 2018)

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions. - Lena Maier-Hein; Matthias Eisenmann; Annika Reinke; Sinan Onogur; Marko Stankovic 002; Patrick Scholz 001; Tal Arbel; Hrvoje Bogunovic; Andrew P. Bradley; Aaron Carass; Carolin Feldmann; Alejandro F. Frangi; et al. (CoRR, 2018)

Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. - Gabriel Maicas; Andrew P. Bradley; Jacinto C. Nascimento; Ian D. Reid 001; Gustavo Carneiro 001 (Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, 2019)

Producing Radiologist-Quality Reports for Interpretable Deep Learning. - William Gale; Luke Oakden-Rayner; Gustavo Carneiro 001; Lyle J. Palmer; Andrew P. Bradley (ISBI, 2019)

Model Agnostic Saliency For Weakly Supervised Lesion Detection From Breast DCE-MRI. - Gabriel Maicas; Gerard Snaauw; Andrew P. Bradley; Ian D. Reid 001; Gustavo Carneiro 001 (ISBI, 2019)

Editorial. - Gustavo Carneiro 001; João Manuel R. S. Tavares; Andrew P. Bradley; João Paulo Papa; Jacinto C. Nascimento; Jaime S. Cardoso 001; Zhi Lu; Vasileios Belagiannis (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2019)

Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN. - Hang Min; Devin Wilson; Yinhuang Huang; Samuel Kelly; Stuart Crozier; Andrew P. Bradley; Shekhar S. Chandra (CoRR, 2019)

Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. - Gabriel Maicas; Andrew P. Bradley; Jacinto C. Nascimento; Ian Reid 001; Gustavo Carneiro 001 (Medical Image Anal., 2019)

Fully Automatic Computer-aided Mass Detection and Segmentation via Pseudo-color Mammograms and Mask R-CNN. - Hang Min; Devin Wilson; Yinhuang Huang; Siyu Liu 002; Stuart Crozier; Andrew P. Bradley; Shekhar S. Chandra (ISBI, 2020)

3D Brain MRI GAN-Based Synthesis Conditioned on Partial Volume Maps. - Filip Rusak; Rodrigo Santa Cruz; Pierrick Bourgeat; Clinton Fookes; Jurgen Fripp; Andrew P. Bradley; Olivier Salvado (SASHIMI@MICCAI, 2020)

Special issue: 4th MICCAI workshop on deep learning in medical image analysis. - Gustavo Carneiro 001; João Manuel R. S. Tavares; Andrew P. Bradley; João Paulo Papa; Vasileios Belagiannis; Jacinto C. Nascimento; Zhi Lu (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2020)

Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI. - Hang Min; Darryl McClymont; Shekhar S. Chandra; Stuart Crozier; Andrew P. Bradley (CoRR, 2020)

Taking the Confusion Out of Multinomial Confusion Matrices and Imbalanced Classes. - David Lovell 002; Bridget McCarron; Brendan Langfield; Khoa Tran; Andrew P. Bradley (AusDM, 2021)

A Comparison of Saliency Methods for Deep Learning Explainability. - Salamata Konate; Léo Lebrat; Rodrigo Santa Cruz; Elliot Smith 003; Andrew P. Bradley; Clinton Fookes; Olivier Salvado (DICTA, 2021)

Smocam: Smooth Conditional Attention Mask For 3d-Regression Models. - Salamata Konate; Léo Lebrat; Rodrigo Santa Cruz; Pierrick Bourgeat; Vincent Doré; Jurgen Fripp; Andrew P. Bradley; Clinton Fookes; Olivier Salvado (ISBI, 2021)

Detail Matters: High-Frequency Content for Realistic Synthetic MRI Generation. - Filip Rusak; Rodrigo Santa Cruz; Elliot Smith 003; Jurgen Fripp; Clinton Fookes; Pierrick Bourgeat; Andrew P. Bradley (SASHIMI@MICCAI, 2021)

Lesser of Two Evils Improves Learning in the Context of Cortical Thickness Estimation Models - Choose Wisely. - Filip Rusak; Rodrigo Santa Cruz; Elliot Smith 003; Jurgen Fripp; Clinton Fookes; Pierrick Bourgeat; Andrew P. Bradley (DALI@MICCAI, 2022)

Never mind the metrics - what about the uncertainty? Visualising confusion matrix metric distributions. - David R. Lovell; Dimity Miller; Jaiden Capra; Andrew P. Bradley (CoRR, 2022)

Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods. - Filip Rusak; Rodrigo Santa Cruz; Léo Lebrat; Ondrej Hlinka; Jurgen Fripp; Elliot Smith 003; Clinton Fookes; Andrew P. Bradley; Pierrick Bourgeat (Medical Image Anal., 2022)

Bias Identification with RankPix Saliency. - Salamata Konate; Léo Lebrat; Rodrigo Santa Cruz; Clinton Fookes; Andrew P. Bradley; Olivier Salvado (ICASSP, 2023)

Never mind the metrics - what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective. - David R. Lovell; Dimity Miller; Jaiden Capra; Andrew P. Bradley (ICML, 2023)

When to Use Augmentation - Variability Insufficient for Cortical Thickness Estimation Improvement. - Filip Rusak; Rodrigo Santa Cruz; Hilda Chourak; Elliot Smith 003; Jurgen Fripp; Clinton Fookes; Pierrick Bourgeat; Andrew P. Bradley (ISBI, 2023)

Designing Interaction with AI for Human Learning: Towards Human-Machine Teaming in Radiology Training. - Margot Brereton; Aloha May Hufana Ambe; David R. Lovell; Laurianne Sitbon; Tara Capel; Alessandro Soro; Yue Xu 001; Catarina Moreira; Benoît Favre; Andrew P. Bradley (OZCHI, 2023)

Localisation of Racial Information in Chest X-Ray for Deep Learning Diagnosis. - Olivier Salvado; Salamata Konate; Rodrigo Santa Cruz; Andrew P. Bradley; Judy Wawira Gichoya; Laleh Seyyed-Kalantari; Brandon J. Price; Clinton Fookes; Léo Lebrat (ISBI, 2024)