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Maximizing citizen scientists' contribution to automated species recognition

Scientific reports, 2022-05, Vol.12 (1), p.7648-7648, Article 7648

2022

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The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with Xpert MTB/RIF in Lusaka Zambia

PloS one, 2014-04, Vol.9 (4), p.e93757

2014

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Suppression of Translucent Elongated Structures: Applications in Chest Radiography

IEEE transactions on medical imaging, 2013-11, Vol.32 (11), p.2099-2113

2013

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Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis

IEEE transactions on medical imaging, 2015-12, Vol.34 (12), p.2429-2442

2015

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Recognizability bias in citizen science photographs

Royal Society open science, 2023-02, Vol.10 (2), p.221063-221063

2023

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Sharing taxonomic expertise between natural history collections using image recognition

Research Ideas and Outcomes, 2022-03, Vol.8, p.115917-20

2022

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Quantifying Local Radiation-Induced Lung Damage From Computed Tomography

International journal of radiation oncology, biology, physics, 2010-02, Vol.76 (2), p.548-556

2010

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Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, p.810-818

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Computer-aided diagnosis in chest radiography: Beyond nodules

European journal of radiology, 2009-11, Vol.72 (2), p.226-230

2009

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Supporting citizen scientists with automatic species identification using deep learning image recognition models

Biodiversity Information Science and Standards, 2018-05, Vol.2, p.e25268

2018

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Adversarial attack vulnerability of medical image analysis systems: Unexplored factors

Medical image analysis, 2021-10, Vol.73, p.102141-102141, Article 102141

2021

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Species Distribution Modelling Using Deep Learning

Biodiversity Information Science and Standards, 2019-07, Vol.3

2019

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Improving Primary Ciliary Dyskinesia Diagnosis Using Artificial Intelligence

Microscopy and microanalysis, 2020-08, Vol.26 (S2), p.2132-2132

2020

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Using Deep Learning in Collection Management to Reduce the Taxonomist’s Workload

Biodiversity Information Science and Standards, 2018-06, Vol.2, p.e25917

2018

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Smart Insect Cameras

Biodiversity Information Science and Standards, 2019-08, Vol.3

2019

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Machine Learning Model for Identifying Dutch/Belgian Biodiversity

Biodiversity Information Science and Standards, 2019-08, Vol.3

2019

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Clavicle segmentation in chest radiographs

Medical image analysis, 2012-12, Vol.16 (8), p.1490-1502

2012

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Fast and effective quantification of symmetry in medical images for pathology detection: Application to chest radiography

Medical physics (Lancaster), 2017-06, Vol.44 (6), p.2242-2256

2017

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Foreign object detection and removal to improve automated analysis of chest radiographs

Medical physics (Lancaster), 2013-07, Vol.40 (7), p.071901-n/a

2013

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Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming: Cavity segmentation in chest radiographs

Medical physics (Lancaster), 2014-06, Vol.41 (7), p.71912

2014

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Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

Medical physics (Lancaster), 2014-07, Vol.41 (7), p.071912-n/a

2014

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Computer-aided diagnosis in chest radiography: Beyond nodules : Digital Radiography

European journal of radiology, 2009, Vol.72 (2), p.226-230

2009

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Deep Learning from Label Proportions for Emphysema Quantification

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, p.768-776

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Fusion of Local and Global Detection Systems to Detect Tuberculosis in Chest Radiographs

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 2010, Vol.13 (Pt 3), p.650-657

2010

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Global and Local Multi-valued Dissimilarity-Based Classification: Application to Computer-Aided Detection of Tuberculosis

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 2009, Vol.12 (Pt 2), p.724-731

2009

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Maximizing citizen scientists' contribution to automated species recognition

The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest X-rays of presumptive TB patients compared with Xpert MTB/RIF in Lusaka Zambia

Suppression of Translucent Elongated Structures: Applications in Chest Radiography

Automatic Detection of Tuberculosis in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis

Recognizability bias in citizen science photographs

Sharing taxonomic expertise between natural history collections using image recognition

Quantifying Local Radiation-Induced Lung Damage From Computed Tomography

Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations

Computer-aided diagnosis in chest radiography: Beyond nodules

Supporting citizen scientists with automatic species identification using deep learning image recognition models

Adversarial attack vulnerability of medical image analysis systems: Unexplored factors

Species Distribution Modelling Using Deep Learning

Improving Primary Ciliary Dyskinesia Diagnosis Using Artificial Intelligence

Using Deep Learning in Collection Management to Reduce the Taxonomist’s Workload

Smart Insect Cameras

Machine Learning Model for Identifying Dutch/Belgian Biodiversity

Clavicle segmentation in chest radiographs

Fast and effective quantification of symmetry in medical images for pathology detection: Application to chest radiography

Foreign object detection and removal to improve automated analysis of chest radiographs

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming: Cavity segmentation in chest radiographs

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

Computer-aided diagnosis in chest radiography: Beyond nodules : Digital Radiography

Deep Learning from Label Proportions for Emphysema Quantification

Fusion of Local and Global Detection Systems to Detect Tuberculosis in Chest Radiographs

Global and Local Multi-valued Dissimilarity-Based Classification: Application to Computer-Aided Detection of Tuberculosis

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