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A feasibility cachaca type recognition using computer vision and pattern recognition

Computers and electronics in agriculture, 2016-04, Vol.123, p.410-414

2016

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Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER

Food research international, 2016-11, Vol.89 (Pt 1), p.504-513

2016

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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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CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

Medical image analysis, 2021-04, Vol.69, p.101950-101950, Article 101950

2021

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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Medical image analysis, 2020-07, Vol.63, p.101693-101693, Article 101693

2020

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Deep learning for chest X-ray analysis: A survey

Medical image analysis, 2021-08, Vol.72, p.102125-102125, Article 102125

2021

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Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Medical image analysis, 2020-07, Vol.63, p.101694-101694, Article 101694

2020

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A deep learning framework for unsupervised affine and deformable image registration

Medical image analysis, 2019-02, Vol.52, p.128-143

2019

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Attention gated networks: Learning to leverage salient regions in medical images

Medical image analysis, 2019-04, Vol.53, p.197-207

2019

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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Medical image analysis, 2017-12, Vol.42, p.1-13

2017

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A survey on active learning and human-in-the-loop deep learning for medical image analysis

Medical image analysis, 2021-07, Vol.71, p.102062-102062, Article 102062

2021

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Image analysis and machine learning in digital pathology: Challenges and opportunities

Medical image analysis, 2016-10, Vol.33, p.170-175

2016

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A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Medical image analysis, 2016-05, Vol.30, p.108-119

2016

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Self-supervised learning for medical image analysis using image context restoration

Medical image analysis, 2019-12, Vol.58, p.101539-101539, Article 101539

2019

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Interpolation consistency training for semi-supervised learning

Neural networks, 2022-01, Vol.145, p.90-106

2022

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BIRNet: Brain image registration using dual-supervised fully convolutional networks

Medical image analysis, 2019-05, Vol.54, p.193-206

2019

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Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Medical image analysis, 2018-08, Vol.48, p.117-130

2018

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Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study

Medical image analysis, 2021-04, Vol.69, p.101952-101952, Article 101952

2021

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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Medical image analysis, 2019-12, Vol.58, p.101537-101537, Article 101537

2019

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3D deeply supervised network for automated segmentation of volumetric medical images

Medical image analysis, 2017-10, Vol.41, p.40-54

2017

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Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images

Medical image analysis, 2021-05, Vol.70, p.101918-101918, Article 101918

2021

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Weakly-supervised convolutional neural networks for multimodal image registration

Medical image analysis, 2018-10, Vol.49, p.1-13

2018

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Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Journal of biomedical informatics, 2016-02, Vol.59, p.49-75

2016

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Machine learning approaches in medical image analysis: From detection to diagnosis

Medical image analysis, 2016-10, Vol.33, p.94-97

de Bruijne, Marleen

2016

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Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation

Medical image analysis, 2017-08, Vol.40, p.172-183

2017

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A feasibility cachaca type recognition using computer vision and pattern recognition

Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER

Large scale deep learning for computer aided detection of mammographic lesions

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Deep learning for chest X-ray analysis: A survey

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

A deep learning framework for unsupervised affine and deformable image registration

Attention gated networks: Learning to leverage salient regions in medical images

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Image analysis and machine learning in digital pathology: Challenges and opportunities

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Self-supervised learning for medical image analysis using image context restoration

Interpolation consistency training for semi-supervised learning

BIRNet: Brain image registration using dual-supervised fully convolutional networks

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

3D deeply supervised network for automated segmentation of volumetric medical images

Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images

Weakly-supervised convolutional neural networks for multimodal image registration

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Machine learning approaches in medical image analysis: From detection to diagnosis

Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation

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