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ORCIDVeronika Cheplygina
Person information
- affiliation: IT University of Copenhagen, Denmark
- affiliation (former): Eindhoven University of Technology, The Netherlands
- affiliation (former): Delft University of Technology, The Netherlands
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2020 – today
- 2025
[e6]Esther Puyol-Antón
, Ghada Zamzmi, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melanie Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Eike Petersen, John S. H. Baxter, Islem Rekik, Roy Eagleson:
Ethics and Fairness in Medical Imaging - Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6-10, 2024, Proceedings. Lecture Notes in Computer Science 15198, Springer 2025, ISBN 978-3-031-72786-3 [contents]- 2024
- [c28]Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim, Veronika Cheplygina:
Dataset Distribution Impacts Model Fairness: Single Vs. Multi-task Learning. FAIMI/EPIMI@MICCAI 2024: 14-23 - [c27]Evangelia Christodoulou, Annika Reinke, Rola Houhou, Piotr Kalinowski, Selen Erkan, Carole H. Sudre, Ninon Burgos, Sofiène Boutaj, Sophie Loizillon, Maëlys Solal, Nicola Rieke, Veronika Cheplygina, Michela Antonelli, Leon D. Mayer, Minu Dietlinde Tizabi, M. Jorge Cardoso, Amber L. Simpson, Paul F. Jäger, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein:
Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI? MICCAI (10) 2024: 124-132 - [i44]Théo Sourget, Ahmet Akkoç, Stinna Winther, Christine Lyngbye Galsgaard, Amelia Jiménez-Sánchez, Dovile Juodelyte, Caroline Petitjean, Veronika Cheplygina:
[Citation needed] Data usage and citation practices in medical imaging conferences. CoRR abs/2402.03003 (2024) - [i43]Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Dovile Juodelyte, Théo Sourget, Caroline Vang-Larsen, Hubert Dariusz Zajac, Veronika Cheplygina:
Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship. CoRR abs/2402.06353 (2024) - [i42]Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, Veronika Cheplygina:
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging. CoRR abs/2403.04484 (2024) - [i41]Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor, Veronika Cheplygina:
Exploring connections of spectral analysis and transfer learning in medical imaging. CoRR abs/2407.11379 (2024) - [i40]Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim, Veronika Cheplygina:
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning. CoRR abs/2407.17543 (2024) - 2023
- [j14]Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina:
Revisiting Hidden Representations in Transfer Learning for Medical Imaging. Trans. Mach. Learn. Res. 2023 (2023) - [c26]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, Sen Yang, Y. Yang, Z. Zhao, Klaus H. Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein:
Why is the Winner the Best? CVPR 2023: 19955-19966 - [c25]Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina:
Detecting Shortcuts in Medical Images - A Case Study in Chest X-Rays. ISBI 2023: 1-5 - [e5]Stefan Wesarg, Esther Puyol-Antón, John S. H. Baxter, Marius Erdt, Klaus Drechsler, Cristina Oyarzun Laura, Moti Freiman, Yufei Chen, Islem Rekik, Roy Eagleson, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melanie Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Daniel Moyer, Eike Petersen:
Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging - 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023 Vancouver, BC, Canada, October 8 and October 12, 2023 Proceedings. Lecture Notes in Computer Science 14242, Springer 2023, ISBN 978-3-031-45248-2 [contents] - [i39]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:
Understanding metric-related pitfalls in image analysis validation. CoRR abs/2302.01790 (2023) - [i38]Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina:
Revisiting Hidden Representations in Transfer Learning for Medical Imaging. CoRR abs/2302.08272 (2023) - [i37]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.:
Why is the winner the best? CoRR abs/2303.17719 (2023) - [i36]Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Veronika Cheplygina, Amelia Jiménez-Sánchez:
Augmenting Chest X-ray Datasets with Non-Expert Annotations. CoRR abs/2309.02244 (2023) - 2022
- [j13]Gaël Varoquaux, Veronika Cheplygina:
Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digit. Medicine 5 (2022) - [j12]Stefan Gaillard, Tara van Viegen, Michele Veldsman, Melanie I. Stefan, Veronika Cheplygina:
Ten simple rules for failing successfully in academia. PLoS Comput. Biol. 18(12): 1010538 (2022) - [c24]Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet:
Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry. KDD 2022: 3107-3115 - [i35]Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina, Paul Honeine, Fahed Abdallah:
Effect of Prior-based Losses on Segmentation Performance: A Benchmark. CoRR abs/2201.02428 (2022) - [i34]Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet:
Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry. CoRR abs/2203.03259 (2022) - [i33]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:
Metrics reloaded: Pitfalls and recommendations for image analysis validation. CoRR abs/2206.01653 (2022) - [i32]Nikolaj Kjøller Bjerregaard, Veronika Cheplygina, Stefan Heinrich:
Detection of Furigana Text in Images. CoRR abs/2207.03960 (2022) - [i31]Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina:
Detecting Shortcuts in Medical Images - A Case Study in Chest X-rays. CoRR abs/2211.04279 (2022) - [i30]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.:
Biomedical image analysis competitions: The state of current participation practice. CoRR abs/2212.08568 (2022) - 2021
- [j11]Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah:
High-level prior-based loss functions for medical image segmentation: A survey. Comput. Vis. Image Underst. 210: 103248 (2021) - [c23]Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim:
Using Uncertainty Estimation To Reduce False Positives In Liver Lesion Detection. ISBI 2021: 663-667 - [c22]Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah:
A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation. MIDL 2021: 158-167 - [i29]Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim:
Using uncertainty estimation to reduce false positives in liver lesion detection. CoRR abs/2101.04386 (2021) - [i28]Gaël Varoquaux, Veronika Cheplygina:
How I failed machine learning in medical imaging - shortcomings and recommendations. CoRR abs/2103.10292 (2021) - [i27]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:
Common Limitations of Image Processing Metrics: A Picture Story. CoRR abs/2104.05642 (2021) - [i26]Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina:
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification. CoRR abs/2107.05940 (2021) - [i25]Ralf Raumanns, Gerard Schouten, Max Joosten, Josien P. W. Pluim, Veronika Cheplygina:
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification. CoRR abs/2107.12734 (2021) - 2020
- [j10]Silas Nyboe Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina:
A Survey of Crowdsourcing in Medical Image Analysis. Hum. Comput. 7: 1-26 (2020) - [j9]Veronika Cheplygina, Felienne Hermans, Casper J. Albers, Natalia Z. Bielczyk, Ionica Smeets:
Ten simple rules for getting started on Twitter as a scientist. PLoS Comput. Biol. 16(2) (2020) - [c21]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:
Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 1 - [c20]Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina:
Risk of Training Diagnostic Algorithms on Data with Demographic Bias. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 183-192 - [c19]Tom van Sonsbeek, Veronika Cheplygina:
Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning. iMIMIC/MIL3ID/LABELS@MICCAI 2020: 242-253 - [e4]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:
Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Lecture Notes in Computer Science 12446, Springer 2020, ISBN 978-3-030-61165-1 [contents] - [i24]Ralf Raumanns, Elif K. Contar, Gerard Schouten, Veronika Cheplygina:
Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis. CoRR abs/2004.14745 (2020) - [i23]Tom van Sonsbeek, Veronika Cheplygina:
Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning. CoRR abs/2005.08869 (2020) - [i22]Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina:
Risk of Training Diagnostic Algorithms on Data with Demographic Bias. CoRR abs/2005.10050 (2020) - [i21]Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina:
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning. CoRR abs/2006.16633 (2020) - [i20]Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah:
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey. CoRR abs/2011.08018 (2020) - [i19]Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne:
Crowdsourcing Airway Annotations in Chest Computed Tomography Images. CoRR abs/2011.10433 (2020)
2010 – 2019
- 2019
- [j8]Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim:
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical Image Anal. 54: 280-296 (2019) - [e3]Luping Zhou, Nicholas Heller, Yiyu Shi, Yiming Xiao, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Xiaobo Sharon Hu, Danny Ziyi Chen, Matthieu Chabanas, Hassan Rivaz, Ingerid Reinertsen:
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention - International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. Lecture Notes in Computer Science 11851, Springer 2019, ISBN 978-3-030-33641-7 [contents] - [i18]Silas Nyboe Ørting, Andrew Doyle, Matthias Hirth, Arno van Hilten, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina:
A Survey of Crowdsourcing in Medical Image Analysis. CoRR abs/1902.09159 (2019) - 2018
- [j7]Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon:
Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognit. 77: 329-353 (2018) - [j6]Veronika Cheplygina, Isabel Pino Peña, Jesper Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne:
Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease. IEEE J. Biomed. Health Informatics 22(5): 1486-1496 (2018) - [c18]Veronika Cheplygina, Josien P. W. Pluim:
Crowd Disagreement About Medical Images Is Informative. CVII-STENT/LABELS@MICCAI 2018: 105-111 - [c17]Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphysema in Chest CT Scans. CVII-STENT/LABELS@MICCAI 2018: 140-149 - [e2]Danail Stoyanov, Zeike Taylor, Simone Balocco, Raphael Sznitman, Anne L. Martel, Lena Maier-Hein, Luc Duong, Guillaume Zahnd, Stefanie Demirci, Shadi Albarqouni, Su-Lin Lee, Stefano Moriconi, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Eric Granger, Pierre Jannin:
Intravascular Imaging and Computer Assisted Stenting - and - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11043, Springer 2018, ISBN 978-3-030-01363-9 [contents] - [i17]Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim:
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. CoRR abs/1804.06353 (2018) - [i16]Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans. CoRR abs/1806.07131 (2018) - [i15]Veronika Cheplygina, Josien P. W. Pluim:
Crowd disagreement of medical images is informative. CoRR abs/1806.08174 (2018) - [i14]Veronika Cheplygina, David M. J. Tax:
Characterizing multiple instance datasets. CoRR abs/1806.08186 (2018) - [i13]Veronika Cheplygina:
Cats or CAT scans: transfer learning from natural or medical image source datasets? CoRR abs/1810.05444 (2018) - 2017
- [c16]Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dashtbozorg, Josien P. W. Pluim:
Exploring the Similarity of Medical Imaging Classification Problems. CVII-STENT/LABELS@MICCAI 2017: 59-66 - [c15]Silas Nyboe Ørting, Veronika Cheplygina, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne:
Crowdsourced Emphysema Assessment. CVII-STENT/LABELS@MICCAI 2017: 126-135 - [e1]M. Jorge Cardoso, Tal Arbel, Su-Lin Lee, Veronika Cheplygina, Simone Balocco, Diana Mateus, Guillaume Zahnd, Lena Maier-Hein, Stefanie Demirci, Eric Granger, Luc Duong, Marc-André Carbonneau, Shadi Albarqouni, Gustavo Carneiro:
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 6th Joint International Workshops, CVII-STENT 2017 and Second International Workshop, LABELS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10-14, 2017, Proceedings. Lecture Notes in Computer Science 10552, Springer 2017, ISBN 978-3-319-67533-6 [contents] - [i12]Veronika Cheplygina, Isabel Pino Peña, Jesper Johannes Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne:
Transfer learning for multi-center classification of chronic obstructive pulmonary disease. CoRR abs/1701.05013 (2017) - [i11]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne:
Classification of COPD with Multiple Instance Learning. CoRR abs/1703.04980 (2017) - [i10]Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne:
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners. CoRR abs/1703.04981 (2017) - [i9]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog:
Label Stability in Multiple Instance Learning. CoRR abs/1703.04986 (2017) - [i8]Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne:
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images. CoRR abs/1706.02051 (2017) - [i7]Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne:
Early Experiences with Crowdsourcing Airway Annotations in Chest CT. CoRR abs/1706.02055 (2017) - [i6]Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dasht Bozorg, Josien P. W. Pluim:
Exploring the similarity of medical imaging classification problems. CoRR abs/1706.03509 (2017) - 2016
- [j5]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. 27(6): 1379-1391 (2016) - [c14]Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne:
Asymmetric similarity-weighted ensembles for image segmentation. ISBI 2016: 273-277 - [c13]Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne:
Early Experiences with Crowdsourcing Airway Annotations in Chest CT. LABELS/DLMIA@MICCAI 2016: 209-218 - [c12]David M. J. Tax, Veronika Cheplygina, Robert P. W. Duin, Jan van de Poll:
The Similarity Between Dissimilarities. S+SSPR 2016: 84-94 - [i5]Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon:
Multiple Instance Learning: A Survey of Problem Characteristics and Applications. CoRR abs/1612.03365 (2016) - 2015
- [b1]Veronika Cheplygina:
Dissimilarity-Based Multiple Instance Learning. Delft University of Technology, Netherlands, 2015 - [j4]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Multiple instance learning with bag dissimilarities. Pattern Recognit. 48(1): 264-275 (2015) - [j3]Ethem Alpaydin, Veronika Cheplygina, Marco Loog, David M. J. Tax:
Single- vs. multiple-instance classification. Pattern Recognit. 48(9): 2831-2838 (2015) - [j2]Veronika Cheplygina, David M. J. Tax, Marco Loog:
On classification with bags, groups and sets. Pattern Recognit. Lett. 59: 11-17 (2015) - [c11]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog:
Label Stability in Multiple Instance Learning. MICCAI (1) 2015: 539-546 - [c10]Veronika Cheplygina, David M. J. Tax:
Characterizing Multiple Instance Datasets. SIMBAD 2015: 15-27 - [c9]Mairelys Hernández-Durán, Veronika Cheplygina, Yenisel Plasencia Calana:
Dissimilarity Representations for Low-Resolution Face Recognition. SIMBAD 2015: 70-83 - 2014
- [c8]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Johannes Holst Pedersen, Marco Loog, Marleen de Bruijne:
Classification of COPD with Multiple Instance Learning. ICPR 2014: 1508-1513 - [c7]Veronika Cheplygina, David M. J. Tax, Marco Loog, Aasa Feragen:
Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder. MLMI 2014: 190-197 - [i4]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Dissimilarity-based Ensembles for Multiple Instance Learning. CoRR abs/1402.1349 (2014) - [i3]David M. J. Tax, Veronika Cheplygina, Marco Loog:
Quantile Representation for Indirect Immunofluorescence Image Classification. CoRR abs/1402.1371 (2014) - [i2]Veronika Cheplygina, David M. J. Tax, Marco Loog:
On Classification with Bags, Groups and Sets. CoRR abs/1406.0281 (2014) - 2013
- [c6]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Combining Instance Information to Classify Bags. MCS 2013: 13-24 - [c5]Yenisel Plasencia Calana, Veronika Cheplygina, Robert P. W. Duin, Edel B. García Reyes, Mauricio Orozco-Alzate, David M. J. Tax, Marco Loog:
On the Informativeness of Asymmetric Dissimilarities. SIMBAD 2013: 75-89 - [i1]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Multiple Instance Learning with Bag Dissimilarities. CoRR abs/1309.5643 (2013) - 2012
- [j1]Wan-Jui Lee, Veronika Cheplygina, David M. J. Tax, Marco Loog, Robert P. W. Duin:
Bridging Structure and Feature Representations in Graph Matching. Int. J. Pattern Recognit. Artif. Intell. 26(5) (2012) - [c4]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Does one rotten apple spoil the whole barrel? ICPR 2012: 1156-1159 - [c3]Veronika Cheplygina, David M. J. Tax, Marco Loog:
Class-Dependent Dissimilarity Measures for Multiple Instance Learning. SSPR/SPR 2012: 602-610 - 2011
- [c2]Veronika Cheplygina, David M. J. Tax:
Pruned Random Subspace Method for One-Class Classifiers. MCS 2011: 96-105 - [c1]David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee:
Bag Dissimilarities for Multiple Instance Learning. SIMBAD 2011: 222-234
Coauthor Index
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