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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

Scientific reports, 2021-04, Vol.11 (1), p.8602-8602, Article 8602

2021

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Exploring COVID-19–Related Stressors: Topic Modeling Study

Journal of medical Internet research, 2022-07, Vol.24 (7), p.e37142-e37142

2022

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Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

European radiology, 2021-01, Vol.31 (1), p.244-255

2021

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Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

Informatics in medicine unlocked, 2021, Vol.27, p.100779-100779, Article 100779

2021

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Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

Scientific reports, 2021-01, Vol.11 (1), p.1378-1378, Article 1378

2021

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Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma

Scientific reports, 2019-04, Vol.9 (1), p.5449-5449, Article 5449

2019

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A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

Journal of digital imaging, 2021-08, Vol.34 (4), p.862-876

2021

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Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

Neuroradiology, 2021-12, Vol.63 (12), p.1957-1967

2021

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Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

BMC medical imaging, 2015-08, Vol.15 (1), p.27-27, Article 27

2015

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Prognostic value of early changes in CT-measured body composition in patients receiving chemotherapy for unresectable pancreatic cancer

European radiology, 2021-11, Vol.31 (11), p.8662-8670

2021

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A Transfer Learning-Based Active Learning Framework for Brain Tumor Classification

Frontiers in artificial intelligence, 2021-05, Vol.4, p.635766-635766

2021

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Using decision curve analysis to benchmark performance of a magnetic resonance imaging–based deep learning model for prostate cancer risk assessment

European radiology, 2020-12, Vol.30 (12), p.6867-6876

2020

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CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis

BMC medical imaging, 2017-06, Vol.17 (1), p.38-38, Article 38

2017

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Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs

Frontiers in radiology, 2022, Vol.2, p.1061402-1061402

2022

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CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging

BMC medical imaging, 2020-02, Vol.20 (1), p.11-11, Article 11

2020

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Improving disease classification performance and explainability of deep learning models in radiology with heatmap generators

Frontiers in radiology, 2022-10, Vol.2, p.991683

2022

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Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Sensors (Basel, Switzerland), 2020-03, Vol.20 (5), p.1539

2020

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A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account

Frontiers in artificial intelligence, 2021-11, Vol.4, p.582928-582928

2021

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MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

BMC medical imaging, 2018-05, Vol.18 (1), p.16-16, Article 16

2018

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Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma

Canadian Association of Radiologists journal, 2023-02, Vol.74 (1), p.119-126

2023

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Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy

Canadian Association of Radiologists journal, 2021-11, Vol.72 (4), p.605-613

2021

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Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning

Frontiers in public health, 2023-02, Vol.11, p.968319-968319

2023

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Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma

Frontiers in artificial intelligence, 2020-10, Vol.3, p.550890-550890

2020

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A Multiphase Validation of Atlas-Based Automatic and Semiautomatic Segmentation Strategies for Prostate MRI

International journal of radiation oncology, biology, physics, 2013, Vol.85 (1), p.95-100

2013

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Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

IEEE access, 2015-01, Vol.3, p.2531-2541

2015

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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

Exploring COVID-19–Related Stressors: Topic Modeling Study

Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks

Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

Prognostic value of early changes in CT-measured body composition in patients receiving chemotherapy for unresectable pancreatic cancer

A Transfer Learning-Based Active Learning Framework for Brain Tumor Classification

Using decision curve analysis to benchmark performance of a magnetic resonance imaging–based deep learning model for prostate cancer risk assessment

CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis

Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs

CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging

Improving disease classification performance and explainability of deep learning models in radiology with heatmap generators

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account

MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning

Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma

A Multiphase Validation of Atlas-Based Automatic and Semiautomatic Segmentation Strategies for Prostate MRI

Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

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