no code implementations • 7 Jul 2020 • Hang Min, Darryl McClymont, Shekhar S. Chandra, Stuart Crozier, Andrew P. Bradley
Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS).
1 code implementation • 28 Jun 2019 • Hang Min, Devin Wilson, Yinhuang Huang, Siyu Liu, Stuart Crozier, Andrew P. Bradley, Shekhar S. Chandra
We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention.
no code implementations • 25 Sep 2018 • Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i. e., it needs a delineation and classification of all lesions in an image).
no code implementations • 20 Jul 2018 • Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneiro
There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpret the DLM classification process.
no code implementations • 6 Jun 2018 • Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider
International challenges have become the standard for validation of biomedical image analysis methods.
no code implementations • 1 Jun 2018 • William Gale, Luke Oakden-Rayner, Gustavo Carneiro, Andrew P. Bradley, Lyle J. Palmer
Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision.
no code implementations • 28 May 2018 • Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro
This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning.
no code implementations • 17 Nov 2017 • William Gale, Luke Oakden-Rayner, Gustavo Carneiro, Andrew P. Bradley, Lyle J. Palmer
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task.
no code implementations • 7 Oct 2016 • Zhi Lu, Gustavo Carneiro, Neeraj Dhungel, Andrew P. Bradley
In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\mu$C).
no code implementations • 1 Jul 2016 • Gustavo Carneiro, Luke Oakden-Rayner, Andrew P. Bradley, Jacinto Nascimento, Lyle Palmer
We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT).
no code implementations • 27 Oct 2014 • Neeraj Dhungel, Gustavo Carneiro, Andrew P. Bradley
In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning.