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Discovery Projects - Grant ID: DP140102794

Title
Discovery Projects - Grant ID: DP140102794
Funding
ARC | Discovery Projects
Contract (GA) number
DP140102794
Start Date
2014/01/01
End Date
2016/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP140102794

 

  • Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

    Carneiro, Gustavo; Oakden-Rayner, Luke; Bradley, Andrew P.; Nascimento, Jacinto; Palmer, Lyle (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP140102794 (DP140102794)
    We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on deep learning, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based ...

    Deep Structured learning for mass segmentation from Mammograms

    Dhungel, Neeraj; Carneiro, Gustavo; Bradley, Andrew P. (2014)
    Projects: ARC | Discovery Projects - Grant ID: DP140102794 (DP140102794)
    In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast...

    Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach

    Lu, Zhi; Carneiro, Gustavo; Dhungel, Neeraj; Bradley, Andrew P. (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP140102794 (DP140102794), ARC | Future Fellowships - Grant ID: FT110100623 (FT110100623)
    In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\mu$C). Currently, the most effective methods are based on three steps: 1) detection of individual $\mu$C candidates, 2) clustering of individual $\mu$C candidates, and 3) classification of $\mu$C clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends ...
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