Search Results for author: Veronika Cheplygina

Found 29 papers, 10 papers with code

ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification

1 code implementation27 Jul 2021 Ralf Raumanns, Gerard Schouten, Max Joosten, Josien P. W. Pluim, Veronika Cheplygina

In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources.

Lesion Classification Multi-Task Learning +1

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification

1 code implementation13 Jul 2021 Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina

There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets.

Fine-tuning Image Classification +1

How I failed machine learning in medical imaging -- shortcomings and recommendations

1 code implementation18 Mar 2021 Gaël Varoquaux, Veronika Cheplygina

Finally we provide a broad range of recommendations on how to further these address problems in the future.

Using uncertainty estimation to reduce false positives in liver lesion detection

no code implementations12 Jan 2021 Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim

We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.

Crowdsourcing Airway Annotations in Chest Computed Tomography Images

1 code implementation20 Nov 2020 Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall.

Computed Tomography (CT)

High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

no code implementations16 Nov 2020 Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks.

Medical Image Segmentation

Risk of Training Diagnostic Algorithms on Data with Demographic Bias

no code implementations20 May 2020 Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina

Surprisingly, we found that papers focusing on diagnosis rarely describe the demographics of the datasets used, and the diagnosis is purely based on images.

Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning

no code implementations8 May 2020 Tom van Sonsbeek, Veronika Cheplygina

Deep learning has led to state-of-the-art results for many medical imaging tasks, such as segmentation of different anatomical structures.

Medical Image Segmentation Meta-Learning

A Survey of Crowdsourcing in Medical Image Analysis

no code implementations25 Feb 2019 Silas Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina

Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis.

Crowd disagreement about medical images is informative

no code implementations21 Jun 2018 Veronika Cheplygina, Josien P. W. Pluim

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds.

Characterizing multiple instance datasets

no code implementations21 Jun 2018 Veronika Cheplygina, David M. J. Tax

When performing a comparison of different MIL classifiers, it is important to understand the differences of the datasets, used in the comparison.

Multiple Instance Learning

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

no code implementations19 Jun 2018 Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

no code implementations7 Jun 2017 Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne

The classifier has a stronger correlation with PFT than the density based method, the percentage of emphysema in the intersection of annotations from both radiologists, and the percentage of emphysema annotated by one of the radiologists.

Multiple Instance Learning

Early Experiences with Crowdsourcing Airway Annotations in Chest CT

no code implementations7 Jun 2017 Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually.

Computed Tomography (CT)

Label Stability in Multiple Instance Learning

no code implementations15 Mar 2017 Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog

We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.

Multiple Instance Learning

Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners

no code implementations15 Mar 2017 Veronika Cheplygina, Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne

We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.

Lesion Segmentation Transfer Learning

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

1 code implementation11 Dec 2016 Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag.

Document Classification Multiple Instance Learning

On Classification with Bags, Groups and Sets

no code implementations2 Jun 2014 Veronika Cheplygina, David M. J. Tax, Marco Loog

To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors.

Classification General Classification

Dissimilarity-based Ensembles for Multiple Instance Learning

no code implementations6 Feb 2014 Veronika Cheplygina, David M. J. Tax, Marco Loog

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors.

Multiple Instance Learning

Quantile Representation for Indirect Immunofluorescence Image Classification

no code implementations6 Feb 2014 David M. J. Tax, Veronika Cheplygina, Marco Loog

Considering one whole slide as a collection (a bag) of feature vectors, however, poses the problem of how to handle this bag.

Classification General Classification +1

Multiple Instance Learning with Bag Dissimilarities

no code implementations22 Sep 2013 Veronika Cheplygina, David M. J. Tax, Marco Loog

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous.

Multiple Instance Learning

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