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D-Index & Metrics

Computer Science

D-Index
44
Citations
18641
World Ranking
7366
National Ranking
234

Research.com Recognitions

  • 2010 - Fellow of the Royal Academy of Engineering (UK)

Overview

Andrew P. Bradley is affiliated with the Queensland University of Technology in Australia. Their research spans primarily across the domains of computer science and medicine, with significant contributions focusing on the intersection of these fields.

The main fields of study for Andrew P. Bradley include:

  • Computer Science
  • Medicine

Within these fields, Bradley has worked extensively on several subfields including:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Cognitive Neuroscience
  • Automotive Engineering

The primary topics covered by Bradley's research comprise:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • AI in cancer detection
  • Autonomous Vehicle Technology and Safety
  • Medical Image Segmentation Techniques
  • Cancer Genomics and Diagnostics
  • Artificial Intelligence in Healthcare and Education

Bradley's work has been published in a variety of venues, often in journals and repositories related to medicine and artificial intelligence. Frequent publication venues include:

  • arXiv (Cornell University)
  • Journal of the American College of Cardiology
  • Genome Medicine
  • The Lancet Digital Health
  • Nature Communications

Selected recent papers authored or co-authored by Andrew P. Bradley include:

  • "Deep learning in cancer diagnosis, prognosis and treatment selection", 2021, Genome Medicine
  • "YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles", 2021, arXiv (Cornell University)
  • "Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study", 2022, The Lancet Digital Health
  • "Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures", 2023, Nature Communications
  • "Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods", 2022, Medical Image Analysis

Collaboration has been a consistent part of Bradley's research activities. Frequent co-authors include:

  • David Lovell
  • Rodrigo Santa Cruz
  • Clinton Fookes
  • Khoa Tran
  • Filip Rusak

Andrew P. Bradley was recognized as a Fellow of the Royal Academy of Engineering (UK) in 2010.

Best Publications

  • The use of the area under the ROC curve in the evaluation of machine learning algorithms

    Andrew P. Bradley

  • Deep learning in cancer diagnosis, prognosis and treatment selection.

    Khoa A. Tran;Olga Kondrashova;Andrew Bradley;Elizabeth D. Williams

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

    M. Jorge Cardoso;Tal Arbel;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Perceptual quality metrics applied to still image compression

    Michael P. Eckert;Andrew P. Bradley

  • Intelligible support vector machines for diagnosis of diabetes mellitus

    Nahla Barakat;Andrew Bradley;Mohamed Barakat

  • A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Why rankings of biomedical image analysis competitions should be interpreted with care

    Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur

  • Unregistered Multiview Mammogram Analysis with Pre-trained Deep Learning Models

    Gustavo Carneiro;Jacinto C. Nascimento;Andrew P. Bradley

  • An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells

    Zhi Lu;Gustavo Carneiro;Andrew P. Bradley

  • Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Rule extraction from support vector machines: A review

    Nahla Barakat;Andrew P. Bradley

  • A wavelet visible difference predictor

    A.P. Bradley

  • Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

    Danail Stoyanov;Zeike Taylor;Gustavo Carneiro;Tanveer Syeda-Mahmood

  • Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning

    Gustavo Carneiro;Jacinto Nascimento;Andrew P. Bradley

  • Deep Learning and Data Labeling for Medical Applications

    Gustavo Carneiro;Diana Mateus;Loïc Peter;Andrew Bradley

  • Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells

    Zhi Lu;Gustavo Carneiro;Andrew P. Bradley;Daniela Ushizima

  • Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

    Luke Oakden-Rayner;Luke Oakden-Rayner;Gustavo Carneiro;Taryn Bessen;Jacinto C. Nascimento

  • Shift-invariance in the Discrete Wavelet Transform

    Andrew P. Bradley

  • The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

    Neeraj Dhungel;Gustavo Carneiro;Andrew P. Bradley

  • Rule Extraction from Support Vector Machines: A Sequential Covering Approach

    Nahla H. Barakat;Andrew P. Bradley

Frequent Co-Authors

Gustavo Carneiro
Gustavo Carneiro University of Surrey
Stuart Crozier
Stuart Crozier University of Queensland
Ian Reid
Ian Reid University of Adelaide
Tal Arbel
Tal Arbel McGill University
Lena Maier-Hein
Lena Maier-Hein German Cancer Research Center
Brian C. Lovell
Brian C. Lovell University of Queensland
Bennett A. Landman
Bennett A. Landman Vanderbilt University
João Paulo Papa
João Paulo Papa Sao Paulo State University
Gregory C. Sharp
Gregory C. Sharp Harvard University
Paul C. Mills
Paul C. Mills University of Queensland

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