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Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Medical image analysis, 2016-05, Vol.30, p.60-71

2016

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Radiology, 2020-05, Vol.295 (2), p.328-338

2020

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Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Medical image analysis, 2014-01, Vol.18 (1), p.176-196

2014

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Assessing radiomics feature stability with simulated CT acquisitions

Scientific reports, 2022-03, Vol.12 (1), p.4732-4732, Article 4732

2022

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Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

1st Edition 2023, 2023

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Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness

Scientific reports, 2019-03, Vol.9 (1), p.4500, Article 4500

2019

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Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

European radiology experimental, 2020-02, Vol.4 (1), p.11-11, Article 11

2020

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The importance of feature aggregation in radiomics: a head and neck cancer study

Scientific reports, 2020-11, Vol.10 (1), p.19679-19679, Article 19679

2020

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Building a reference multimedia database for interstitial lung diseases

Computerized medical imaging and graphics, 2012-04, Vol.36 (3), p.227-238

2012

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Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Computer methods in biomechanics and biomedical engineering., 2018-01, Vol.6 (1), p.1-6

2018

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Head and Neck Tumor Segmentation and Outcome Prediction: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

1, 2022

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Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

1, 2021

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Wide kernels and their DCT compression in convolutional networks for nuclei segmentation

Informatics in medicine unlocked, 2023, Vol.43, p.101403, Article 101403

2023

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Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Investigative radiology, 2015-04, Vol.50 (4), p.261-267

2015

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Biomedical Texture Analysis: Fundamentals, Tools and Challenges

1, 2017

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On the Scale Invariance in State of the Art CNNs Trained on ImageNet

Machine learning and knowledge extraction, 2021-06, Vol.3 (2), p.374-391

2021

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Making sense of radiomics: insights on human–AI collaboration in medical interaction from an observational user study

Frontiers in communication, 2024-02, Vol.8

2024

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Lung Texture Classification Using Locally–Oriented Riesz Components

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, 2011, Vol.14 (Pt 3), p.231-238

2011

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How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

NeuroImage clinical, 2023-01, Vol.39, p.103491-103491, Article 103491

2023

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Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

European journal of hybrid imaging, 2022-10, Vol.6 (1), p.33-33, Article 33

2022

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QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

European radiology experimental, 2023-03, Vol.7 (1), p.16-16, Article 16

2023

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Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study

Clinical and translational radiation oncology, 2022-03, Vol.33, p.153-158

2022

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Multiscale Lung Texture Signature Learning Using the Riesz Transform

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 2012, Vol.15 (Pt 3), p.517-524

2012

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Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification

IEEE transactions on image processing, 2017-04, Vol.26 (4), p.1626-1636

2017

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Local rotation invariance in 3D CNNs

Medical image analysis, 2020-10, Vol.65, p.101756-101756, Article 101756

2020

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Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Assessing radiomics feature stability with simulated CT acquisitions

Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

The importance of feature aggregation in radiomics: a head and neck cancer study

Building a reference multimedia database for interstitial lung diseases

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Head and Neck Tumor Segmentation and Outcome Prediction: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

Wide kernels and their DCT compression in convolutional networks for nuclei segmentation

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Biomedical Texture Analysis: Fundamentals, Tools and Challenges

On the Scale Invariance in State of the Art CNNs Trained on ImageNet

Making sense of radiomics: insights on human–AI collaboration in medical interaction from an observational user study

Lung Texture Classification Using Locally–Oriented Riesz Components

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study

Multiscale Lung Texture Signature Learning Using the Riesz Transform

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification

Local rotation invariance in 3D CNNs

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