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Large scale deep learning for computer aided detection of mammographic lesions

Medical image analysis, 2017-01, Vol.35, p.303-312

2017

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Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

Medical physics (Lancaster), 2017-03, Vol.44 (3), p.1017-1027

2017

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Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

Journal of medical imaging (Bellingham, Wash.), 2017-10, Vol.4 (4), p.044501-044501

2017

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Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction

Diagnostics (Basel), 2024-06, Vol.14 (12), p.1212

2024

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A survey on deep learning in medical image analysis

Medical image analysis, 2017-12, Vol.42, p.60-88

2017

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Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time

Radiology. Artificial intelligence, 2024-05, Vol.6 (3), p.e230318

2024

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Understanding metric-related pitfalls in image analysis validation

Nature methods, 2024-02, Vol.21 (2), p.182

2024

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Transformer-based Deep Neural Network for Breast Cancer Classification on Digital Breast Tomosynthesis Images

Radiology. Artificial intelligence, 2023-05, Vol.5 (3), p.e220159-e220159

2023

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Fully automated detection of breast cancer in screening MRI using convolutional neural networks

Journal of medical imaging (Bellingham, Wash.), 2018-01, Vol.5 (1), p.014502-014502

2018

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Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography

Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, p.55-64

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Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

Breast Imaging, p.35-42

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OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs

Applications of Medical Artificial Intelligence, 2022, Vol.13540, p.139-149

2022

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Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, p.389-398

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A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography

Breast Imaging, p.51-56

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Invariant Features for Discriminating Cysts from Solid Lesions in Mammography

Breast Imaging, p.573-580

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Computer Vision - ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012. Revised Selected Papers, Part II

2013 edition., 2013

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Colour Descriptors for Tracking in Spatial Augmented Reality

Computer Vision - ACCV 2012 Workshops, p.387-399

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Region enhanced neural Q-learning for solving model-based POMDPs

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, p.1-8

2010

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Requirement of Signal Peptidase ComC and Thiol-Disulfide Oxidoreductase DsbA for Optimal Cell Surface Display of Pseudopilin ComGC in Staphylococcus aureus

Applied and Environmental Microbiology, 2012-10, Vol.78 (19), p.7124-7127

2012

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Faeces as a novel material to estimate lyssavirus prevalence in bat populations

Zoonoses and public health, 2020-03, Vol.67 (2), p.198-202

2020

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Large scale deep learning for computer aided detection of mammographic lesions

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction

A survey on deep learning in medical image analysis

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time

Understanding metric-related pitfalls in image analysis validation

Transformer-based Deep Neural Network for Breast Cancer Classification on Digital Breast Tomosynthesis Images

Fully automated detection of breast cancer in screening MRI using convolutional neural networks

Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer in Mammography

Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs

Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography

Invariant Features for Discriminating Cysts from Solid Lesions in Mammography

Computer Vision - ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012. Revised Selected Papers, Part II

Colour Descriptors for Tracking in Spatial Augmented Reality

Region enhanced neural Q-learning for solving model-based POMDPs

Requirement of Signal Peptidase ComC and Thiol-Disulfide Oxidoreductase DsbA for Optimal Cell Surface Display of Pseudopilin ComGC in Staphylococcus aureus

Faeces as a novel material to estimate lyssavirus prevalence in bat populations

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