Veronika Cheplygina

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TU Eindhoven ∙ Delft University of Technology

Assistant Professor at the Medical Image Analysis group at Eindhoven University of Technology

Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

Early detection of lung cancer has been proven to decrease mortality sig...
06/30/2020 ∙ by Linde S. Hesse, et al. ∙ 4 ∙ share

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Risk of Training Diagnostic Algorithms on Data with Demographic Bias

One of the critical challenges in machine learning applications is to ha...
05/20/2020 ∙ by Samaneh Abbasi-Sureshjani, et al. ∙ 3 ∙ share

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Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis

Machine learning has a recognised need for large amounts of annotated da...
04/28/2020 ∙ by Ralf Raumanns, et al. ∙ 7 ∙ share

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A Survey of Crowdsourcing in Medical Image Analysis

Rapid advances in image processing capabilities have been seen across ma...
02/25/2019 ∙ by Silas Ørting, et al. ∙ 28 ∙ share

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Cats or CAT scans: transfer learning from natural or medical image source datasets?

Transfer learning is a widely used strategy in medical image analysis. I...
10/12/2018 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Characterizing multiple instance datasets

In many pattern recognition problems, a single feature vector is not suf...
06/21/2018 ∙ by Veronika Cheplygina, et al. ∙ 8 ∙ share

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Crowd disagreement of medical images is informative

Classifiers for medical image analysis are often trained with a single c...
06/21/2018 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

Supervised feature learning using convolutional neural networks (CNNs) c...
06/19/2018 ∙ by Silas Nyboe Ørting, et al. ∙ 12 ∙ share

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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Machine learning (ML) algorithms have made a tremendous impact in the fi...
04/17/2018 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this pap...
06/12/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Early Experiences with Crowdsourcing Airway Annotations in Chest CT

Measuring airways in chest computed tomography (CT) images is important ...
06/07/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

A method for automatically quantifying emphysema regions using High-Reso...
06/07/2017 ∙ by Isabel Pino Peña, et al. ∙ 0 ∙ share

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Label Stability in Multiple Instance Learning

We address the problem of instance label stability in multiple instance ...
03/15/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

Supervised learning has been very successful for automatic segmentation ...
03/15/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Classification of COPD with Multiple Instance Learning

Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...
03/15/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Transfer learning for multi-center classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can...
01/18/2017 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning...
12/11/2016 ∙ by Marc-André Carbonneau, et al. ∙ 0 ∙ share

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On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in t...
06/02/2014 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Quantile Representation for Indirect Immunofluorescence Image Classification

In the diagnosis of autoimmune diseases, an important task is to classif...
02/06/2014 ∙ by David M. J. Tax, et al. ∙ 0 ∙ share

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Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vector...
02/06/2014 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Multiple Instance Learning with Bag Dissimilarities

Multiple instance learning (MIL) is concerned with learning from sets (b...
09/22/2013 ∙ by Veronika Cheplygina, et al. ∙ 0 ∙ share

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Veronika Cheplygina

Featured Co-authors

Marco Loog

Marleen de Bruijne

Eric Granger

Josien P. W. Pluim

Jens Petersen

David M. J. Tax

Mitko Veta

Lauge Sørensen

Pim Moeskops

Pim A. de Jong

M. Arfan Ikram

Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

Risk of Training Diagnostic Algorithms on Data with Demographic Bias

Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis

A Survey of Crowdsourcing in Medical Image Analysis

Cats or CAT scans: transfer learning from natural or medical image source datasets?

Characterizing multiple instance datasets

Crowd disagreement of medical images is informative

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Exploring the similarity of medical imaging classification problems

Early Experiences with Crowdsourcing Airway Annotations in Chest CT

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

Label Stability in Multiple Instance Learning

Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

Classification of COPD with Multiple Instance Learning

Transfer learning for multi-center classification of chronic obstructive pulmonary disease

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

On Classification with Bags, Groups and Sets

Quantile Representation for Indirect Immunofluorescence Image Classification

Dissimilarity-based Ensembles for Multiple Instance Learning

Multiple Instance Learning with Bag Dissimilarities