Veronika Cheplygina

According to our database, Veronika Cheplygina authored at least 64 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging.

Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, Veronika Cheplygina

CoRR, 2024

Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship.

Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Dovile Juodelyte, Théo Sourget, Caroline Vang-Larsen, Hubert Dariusz Zajac, Veronika Cheplygina

CoRR, 2024

[Citation needed] Data usage and citation practices in medical imaging conferences.

Théo Sourget, Ahmet Akkoç, Stinna Winther, Christine Lyngbye Galsgaard, Amelia Jiménez-Sánchez, Dovile Juodelyte, Caroline Petitjean, Veronika Cheplygina

CoRR, 2024

2023

Augmenting Chest X-ray Datasets with Non-Expert Annotations.

Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Veronika Cheplygina, Amelia Jiménez-Sánchez

CoRR, 2023

Why is the winner the best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldrich Kodym, Michal Kozubek, Jianning Li, Hongwei Bran Li, Jun Ma, Carlos Martín-Isla, Bjoern H. Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, et al.

CoRR, 2023

Revisiting Hidden Representations in Transfer Learning for Medical Imaging.

Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina

CoRR, 2023

Understanding metric-related pitfalls in image analysis validation.

Annika Reinke, Minu Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew B. Blaschko, Florian Büttner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir M. Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

CoRR, 2023

Detecting Shortcuts in Medical Images - A Case Study in Chest X-Rays.

Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Why is the Winner the Best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek, Jianning Li, Hongwei Bran Li, Jun Ma, Carlos Martín-Isla, Bjoern H. Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Q. Dou, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo Jaco Kuijf, M. Li, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, S. Pang, J. Park, S. Park, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, H. Wang, J. Wang, L. Wang, X. Wang, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, S. Yang, Y. Yang, Z. Zhao, Klaus H. Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Ten simple rules for failing successfully in academia.

Stefan Gaillard, Tara van Viegen, Michele Veldsman, Melanie I. Stefan, Veronika Cheplygina

PLoS Comput. Biol., December, 2022

Machine learning for medical imaging: methodological failures and recommendations for the future.

Gaël Varoquaux, Veronika Cheplygina

npj Digit. Medicine, 2022

Biomedical image analysis competitions: The state of current participation practice.

Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, J. Alison Noble, Carlos Ortiz-de-Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett A. Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne L. Martel, et al.

CoRR, 2022

Detection of Furigana Text in Images.

Nikolaj Kjøller Bjerregaard, Veronika Cheplygina, Stefan Heinrich

CoRR, 2022

Metrics reloaded: Pitfalls and recommendations for image analysis validation.

Lena Maier-Hein, Annika Reinke, Evangelia Christodoulou, Ben Glocker, Patrick Godau, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Minu Dietlinde Tizabi, Laura Ación, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Annette Kopp-Schneider, Anna Kreshuk, Tahsin M. Kurç, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus H. Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern H. Menze, David Moher, Karel G. M. Moons, Henning Müller, Felix Nickel, Brennan Nichyporuk, Jens Petersen, Nasir M. Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clarisa Sánchez Gutiérrez, Shravya Shetty, Maarten van Smeden, Carole H. Sudre, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Paul F. Jäger

CoRR, 2022

Effect of Prior-based Losses on Segmentation Performance: A Benchmark.

Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina, Paul Honeine, Fahed Abdallah

CoRR, 2022

Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry.

Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

High-level prior-based loss functions for medical image segmentation: A survey.

Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah

Comput. Vis. Image Underst., 2021

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

Ralf Raumanns, Gerard Schouten, Max Joosten, Josien P. W. Pluim, Veronika Cheplygina

CoRR, 2021

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

Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina

CoRR, 2021

Common Limitations of Image Processing Metrics: A Picture Story.

Annika Reinke, Matthias Eisenmann, Minu Dietlinde Tizabi, Carole H. Sudre, Tim Rädsch, Michela Antonelli, Tal Arbel, Spyridon Bakas, M. Jorge Cardoso, Veronika Cheplygina, Keyvan Farahani, Ben Glocker, Doreen Heckmann-Nötzel, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Jens Kleesiek, Tahsin M. Kurç, Michal Kozubek, Bennett A. Landman, Geert Litjens, Klaus H. Maier-Hein, Bjoern H. Menze, Henning Müller, Jens Petersen, Mauricio Reyes, Nicola Rieke, Bram Stieltjes, Ronald M. Summers, Sotirios A. Tsaftaris, Bram van Ginneken, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

CoRR, 2021

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

Gaël Varoquaux, Veronika Cheplygina

CoRR, 2021

A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation.

Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Using Uncertainty Estimation To Reduce False Positives In Liver Lesion Detection.

Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

2020

Ten simple rules for getting started on Twitter as a scientist.

Veronika Cheplygina, Felienne Hermans, Casper J. Albers, Natalia Z. Bielczyk, Ionica Smeets

PLoS Comput. Biol., 2020

A Survey of Crowdsourcing in Medical Image Analysis.

Silas Nyboe Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina

Hum. Comput., 2020

Crowdsourcing Airway Annotations in Chest Computed Tomography Images.

Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

CoRR, 2020

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

Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina

CoRR, 2020

Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis.

Ralf Raumanns, Elif K. Contar, Gerard Schouten, Veronika Cheplygina

CoRR, 2020

Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning.

Tom van Sonsbeek, Veronika Cheplygina

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing.

Jaime S. Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo P. M. Cruz, José Pereira Amorim, Vishal Patel, Badri Roysam, S. Kevin Zhou, Steve B. Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

Risk of Training Diagnostic Algorithms on Data with Demographic Bias.

Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

2019

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

Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim

Medical Image Anal., 2019

2018

Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease.

Veronika Cheplygina, Isabel Pino Peña, Jesper Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne

IEEE J. Biomed. Health Informatics, 2018

Multiple instance learning: A survey of problem characteristics and applications.

Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon

Pattern Recognit., 2018

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

Veronika Cheplygina

CoRR, 2018

Crowd disagreement of medical images is informative.

Veronika Cheplygina, Josien P. W. Pluim

CoRR, 2018

Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphysema in Chest CT Scans.

Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

Proceedings of the Intravascular Imaging and Computer Assisted Stenting - and - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018

Crowd Disagreement About Medical Images Is Informative.

Veronika Cheplygina, Josien P. W. Pluim

Proceedings of the Intravascular Imaging and Computer Assisted Stenting - and - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018

2017

Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images.

Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne

CoRR, 2017

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

Veronika Cheplygina, Isabel Pino Peña, Jesper Johannes Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne

CoRR, 2017

Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners.

Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne

CoRR, 2017

Crowdsourced Emphysema Assessment.

Silas Nyboe Ørting, Veronika Cheplygina, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

Proceedings of the Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2017

Exploring the Similarity of Medical Imaging Classification Problems.

Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dashtbozorg, Josien P. W. Pluim

Proceedings of the Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2017

2016

Dissimilarity-Based Ensembles for Multiple Instance Learning.

Veronika Cheplygina, David M. J. Tax, Marco Loog

IEEE Trans. Neural Networks Learn. Syst., 2016

The Similarity Between Dissimilarities.

David M. J. Tax, Veronika Cheplygina, Robert P. W. Duin, Jan van de Poll

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2016

Early Experiences with Crowdsourcing Airway Annotations in Chest CT.

Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

Proceedings of the Deep Learning and Data Labeling for Medical Applications, 2016

Asymmetric similarity-weighted ensembles for image segmentation.

Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne

Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 2016

2015

Dissimilarity-Based Multiple Instance Learning.

Veronika Cheplygina

PhD thesis, 2015

On classification with bags, groups and sets.

Veronika Cheplygina, David M. J. Tax, Marco Loog

Pattern Recognit. Lett., 2015

Multiple instance learning with bag dissimilarities.

Veronika Cheplygina, David M. J. Tax, Marco Loog

Pattern Recognit., 2015

Single- vs. multiple-instance classification.

Ethem Alpaydin, Veronika Cheplygina, Marco Loog, David M. J. Tax

Pattern Recognit., 2015

Dissimilarity Representations for Low-Resolution Face Recognition.

Mairelys Hernández-Durán, Veronika Cheplygina, Yenisel Plasencia Calana

Proceedings of the Similarity-Based Pattern Recognition - Third International Workshop, 2015

Characterizing Multiple Instance Datasets.

Veronika Cheplygina, David M. J. Tax

Proceedings of the Similarity-Based Pattern Recognition - Third International Workshop, 2015

Label Stability in Multiple Instance Learning.

Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015

2014

Quantile Representation for Indirect Immunofluorescence Image Classification.

David M. J. Tax, Veronika Cheplygina, Marco Loog

CoRR, 2014

Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder.

Veronika Cheplygina, David M. J. Tax, Marco Loog, Aasa Feragen

Proceedings of the Machine Learning in Medical Imaging - 5th International Workshop, 2014

Classification of COPD with Multiple Instance Learning.

Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Johannes Holst Pedersen, Marco Loog, Marleen de Bruijne

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

2013

On the Informativeness of Asymmetric Dissimilarities.

Yenisel Plasencia Calana, Veronika Cheplygina, Robert P. W. Duin, Edel B. García Reyes, Mauricio Orozco-Alzate, David M. J. Tax, Marco Loog

Proceedings of the Similarity-Based Pattern Recognition - Second International Workshop, 2013

Combining Instance Information to Classify Bags.

Veronika Cheplygina, David M. J. Tax, Marco Loog

Proceedings of the Multiple Classifier Systems, 11th International Workshop, 2013

2012

Bridging Structure and Feature Representations in Graph Matching.

Wan-Jui Lee, Veronika Cheplygina, David M. J. Tax, Marco Loog, Robert P. W. Duin

Int. J. Pattern Recognit. Artif. Intell., 2012

Class-Dependent Dissimilarity Measures for Multiple Instance Learning.

Veronika Cheplygina, David M. J. Tax, Marco Loog

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2012

Does one rotten apple spoil the whole barrel?

Veronika Cheplygina, David M. J. Tax, Marco Loog

Proceedings of the 21st International Conference on Pattern Recognition, 2012

2011

Bag Dissimilarities for Multiple Instance Learning.

David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee

Proceedings of the Similarity-Based Pattern Recognition - First International Workshop, 2011

Pruned Random Subspace Method for One-Class Classifiers.

Veronika Cheplygina, David M. J. Tax

Proceedings of the Multiple Classifier Systems - 10th International Workshop, 2011