Veronika Cheplygina

113 papers A* 3B 2Journal 70Unranked 30

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Dataset Diversity Metrics and Impact on Classification Models. CoRR Théo Sourget, Niclas Claßen, Jack Junchi Xu, Rob van der Goot, Veronika Cheplygina
2026	—	`ed.`	Fairness of AI in Medical Imaging - Third International Workshop, FAIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings FAIMI@MICCAI Esther Puyol-Antón, Enzo Ferrante, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melanie Ganz-Benjaminsen, Ben Glocker, Eike Petersen, Heisook Lee
2025	—	`conf`	Augmenting Chest X-ray Datasets with Non-Expert Annotations. MIUA (1) Veronika Cheplygina, Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Amelia Jiménez-Sánchez
2025	—	`ed.`	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 FAIMI/EPIMI@MICCAI 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
2025	J	`jnl`	False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims. CoRR Evangelia Christodoulou, Annika Reinke, Pascaline Andrè, Patrick Godau, Piotr Kalinowski, Rola Houhou, Selen Erkan, Carole H. Sudre, Ninon Burgos, Sofiène Boutaj, Sophie Loizillon, Maëlys Solal, Veronika Cheplygina, Charles Heitz, Michal Kozubek, Michela Antonelli, Nicola Rieke, Antoine Gilson, Leon D. Mayer, Minu Dietlinde Tizabi, Manuel Jorge Cardoso, Amber L. Simpson, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein
2025	—	`conf`	In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. FAccT Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Sarah de Boer, Víctor M. Campello, Aasa Feragen, Enzo Ferrante, Melanie Ganz, Judy Wawira Gichoya, Camila González, Steff Groefsema, Alessa Hering, Adam Hulman, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, André G. C. Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zajac, Maria A. Zuluaga, Veronika Cheplygina
2025	J	`jnl`	In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. CoRR Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Sarah de Boer, Víctor M. Campello, Aasa Feragen, Enzo Ferrante, Melanie Ganz, Judy Wawira Gichoya, Camila González, Steff Groefsema, Alessa Hering, Adam Hulman, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, André G. C. Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zajac, Maria A. Zuluaga, Veronika Cheplygina
2025	J	`jnl`	Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification. CoRR Yucheng Lu, Hubert Dariusz Zajac, Veronika Cheplygina, Amelia Jiménez-Sánchez
2025	—	`conf`	Learning to Harmonize Cross-Vendor X-ray Images by Non-linear Image Dynamics Correction. MIUA (2) Yucheng Lu, Shunxin Wang, Dovile Juodelyte, Veronika Cheplygina
2025	J	`jnl`	Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction. CoRR Yucheng Lu, Shunxin Wang, Dovile Juodelyte, Veronika Cheplygina
2025	J	`jnl`	Medical Imaging AI Competitions Lack Fairness. CoRR Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda, A. Emre Kavur, Khrystyna Faryna, Daan Schouten, Bennett A. Landman, Carole H. Sudre, Olivier Colliot, Nicholas Heller, Sophie Loizillon, Martin Maska, Maëlys Solal, Arya Yazdan Panah, Vilma Bozgo, Ömer Sümer, Siem de Jong, Sophie Fischer, Michal Kozubek, Tim Rädsch, Nadim Hammoud, Fruzsina Molnár-Gábor, Steven Hicks, Michael A. Riegler, Anindo Saha, Vajira Thambawita, Pål Halvorsen, Amelia Jiménez-Sánchez, Qingyang Yang, Veronika Cheplygina, Sabrina Bottazzi, Alexander Seitel, Spyridon Bakas, Alexandros Karargyris, Kiran Vaidhya Venkadesh, Bram van Ginneken, Lena Maier-Hein
2025	—	`conf`	Robustness and Sex Differences in Skin Cancer Detection: Logistic Regression vs CNNs. FAIMI@MICCAI Nikolette Pedersen, Regitze Sydendal, Andreas Wulff, Ralf Raumanns, Eike Petersen, Veronika Cheplygina
2025	J	`jnl`	Robustness and sex differences in skin cancer detection: logistic regression vs CNNs. CoRR Nikolette Pedersen, Regitze Sydendal, Andreas Wulff, Ralf Raumanns, Eike Petersen, Veronika Cheplygina
2025	J	`jnl`	SeagrassFinder: Deep learning for eelgrass detection and coverage estimation in the wild. Ecol. Informatics Jannik Elsäßer, Laura Weihl, Veronika Cheplygina, Lisbeth Tangaa Nielsen
2024	—	`conf`	Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI? MICCAI (10) 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
2024	J	`jnl`	Confidence intervals uncovered: Are we ready for real-world medical imaging AI? CoRR 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, Manuel Jorge Cardoso, Amber L. Simpson, Paul F. Jäger, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein
2024	A*	`conf`	Copycats: the many lives of a publicly available medical imaging dataset. NeurIPS Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Dovile Juodelyte, Théo Sourget, Caroline Vang-Larsen, Anna Rogers, Hubert Dariusz Zajac, Veronika Cheplygina
2024	—	`conf`	Dataset Distribution Impacts Model Fairness: Single Vs. Multi-task Learning. FAIMI/EPIMI@MICCAI Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim, Veronika Cheplygina
2024	J	`jnl`	Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning. CoRR Ralf Raumanns, Gerard Schouten, Josien P. W. Pluim, Veronika Cheplygina
2024	J	`jnl`	Exploring connections of spectral analysis and transfer learning in medical imaging. CoRR Yucheng Lu, Dovile Juodelyte, Jonathan D. Victor, Veronika Cheplygina
2024	J	`jnl`	Mask of truth: model sensitivity to unexpected regions of medical images. CoRR Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez, Jack Junchi Xu, Veronika Cheplygina
2024	J	`jnl`	On dataset transferability in medical image classification. CoRR Dovile Juodelyte, Enzo Ferrante, Yucheng Lu, Prabhant Singh, Joaquin Vanschoren, Veronika Cheplygina
2024	J	`jnl`	SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild. CoRR Jannik Elsäßer, Laura Weihl, Veronika Cheplygina, Lisbeth Tangaa Nielsen
2024	—	`conf`	Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging. AMAI@MICCAI Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, Veronika Cheplygina
2024	J	`jnl`	Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging. CoRR Dovile Juodelyte, Yucheng Lu, Amelia Jiménez-Sánchez, Sabrina Bottazzi, Enzo Ferrante, Veronika Cheplygina
2024	J	`jnl`	Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship. CoRR Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Dovile Juodelyte, Théo Sourget, Caroline Vang-Larsen, Hubert Dariusz Zajac, Veronika Cheplygina
2024	—	`conf`	[Citation needed] Data usage and citation practices in medical imaging conferences. MIDL Théo Sourget, Ahmet Akkoç, Stinna Winther, Christine Lyngbye Galsgaard, Amelia Jiménez-Sánchez, Dovile Juodelyte, Caroline Petitjean, Veronika Cheplygina
2024	J	`jnl`	[Citation needed] Data usage and citation practices in medical imaging conferences. CoRR Théo Sourget, Ahmet Akkoç, Stinna Winther, Christine Lyngbye Galsgaard, Amelia Jiménez-Sánchez, Dovile Juodelyte, Caroline Petitjean, Veronika Cheplygina
2023	J	`jnl`	Augmenting Chest X-ray Datasets with Non-Expert Annotations. CoRR Cathrine Damgaard, Trine Naja Eriksen, Dovile Juodelyte, Veronika Cheplygina, Amelia Jiménez-Sánchez
2023	—	`ed.`	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 CLIP/FAIMI/EPIMI@MICCAI 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
2023	—	`conf`	Detecting Shortcuts in Medical Images - A Case Study in Chest X-Rays. ISBI Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina
2023	J	`jnl`	Revisiting Hidden Representations in Transfer Learning for Medical Imaging. CoRR Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina
2023	J	`jnl`	Revisiting Hidden Representations in Transfer Learning for Medical Imaging. Trans. Mach. Learn. Res. Dovile Juodelyte, Amelia Jiménez-Sánchez, Veronika Cheplygina
2023	J	`jnl`	Understanding metric-related pitfalls in image analysis validation. CoRR 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
2023	A*	`conf`	Why is the Winner the Best? CVPR 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, Zixuan Zhao, Klaus H. Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein
2023	J	`jnl`	Why is the winner the best? CoRR 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.
2022	J	`jnl`	Biomedical image analysis competitions: The state of current participation practice. CoRR 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.
2022	J	`jnl`	Detecting Shortcuts in Medical Images - A Case Study in Chest X-rays. CoRR Amelia Jiménez-Sánchez, Dovile Juodelyte, Bethany Chamberlain, Veronika Cheplygina
2022	J	`jnl`	Detection of Furigana Text in Images. CoRR Nikolaj Kjøller Bjerregaard, Veronika Cheplygina, Stefan Heinrich
2022	J	`jnl`	Effect of Prior-based Losses on Segmentation Performance: A Benchmark. CoRR Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina, Paul Honeine, Fahed Abdallah
2022	J	`jnl`	Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digit. Medicine Gaël Varoquaux, Veronika Cheplygina
2022	J	`jnl`	Metrics reloaded: Pitfalls and recommendations for image analysis validation. CoRR 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
2022	A*	`conf`	Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry. KDD Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet
2022	J	`jnl`	Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry. CoRR Dovile Juodelyte, Veronika Cheplygina, Therese Graversen, Philippe Bonnet
2022	J	`jnl`	Ten simple rules for failing successfully in academia. PLoS Comput. Biol. Stefan Gaillard, Tara van Viegen, Michele Veldsman, Melanie I. Stefan, Veronika Cheplygina
2021	—	`conf`	A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation. MIDL Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah
2021	J	`jnl`	Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification. CoRR Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina
2021	J	`jnl`	Common Limitations of Image Processing Metrics: A Picture Story. CoRR 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
2021	J	`jnl`	ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification. CoRR Ralf Raumanns, Gerard Schouten, Max Joosten, Josien P. W. Pluim, Veronika Cheplygina
2021	J	`jnl`	High-level prior-based loss functions for medical image segmentation: A survey. Comput. Vis. Image Underst. Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah
2021	J	`jnl`	How I failed machine learning in medical imaging - shortcomings and recommendations. CoRR Gaël Varoquaux, Veronika Cheplygina
2021	—	`conf`	Using Uncertainty Estimation To Reduce False Positives In Liver Lesion Detection. ISBI Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim
2021	J	`jnl`	Using uncertainty estimation to reduce false positives in liver lesion detection. CoRR Ishaan Bhat, Hugo J. Kuijf, Veronika Cheplygina, Josien P. W. Pluim
2020	J	`jnl`	A Survey of Crowdsourcing in Medical Image Analysis. Hum. Comput. Silas Nyboe Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina
2020	—	`conf`	Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing. iMIMIC/MIL3ID/LABELS@MICCAI 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
2020	J	`jnl`	Crowdsourcing Airway Annotations in Chest Computed Tomography Images. CoRR Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
2020	J	`jnl`	High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey. CoRR Rosana El Jurdi, Caroline Petitjean, Paul Honeine, Veronika Cheplygina, Fahed Abdallah
2020	—	`ed.`	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 iMIMIC/MIL3ID/LABELS@MICCAI 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
2020	J	`jnl`	Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis. CoRR Ralf Raumanns, Elif K. Contar, Gerard Schouten, Veronika Cheplygina
2020	J	`jnl`	Predicting Scores of Medical Imaging Segmentation Methods with Meta-Learning. CoRR Tom van Sonsbeek, Veronika Cheplygina
2020	—	`conf`	Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning. iMIMIC/MIL3ID/LABELS@MICCAI Tom van Sonsbeek, Veronika Cheplygina
2020	J	`jnl`	Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning. CoRR Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina
2020	—	`conf`	Risk of Training Diagnostic Algorithms on Data with Demographic Bias. iMIMIC/MIL3ID/LABELS@MICCAI Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina
2020	J	`jnl`	Risk of Training Diagnostic Algorithms on Data with Demographic Bias. CoRR Samaneh Abbasi-Sureshjani, Ralf Raumanns, Britt E. J. Michels, Gerard Schouten, Veronika Cheplygina
2020	J	`jnl`	Ten simple rules for getting started on Twitter as a scientist. PLoS Comput. Biol. Veronika Cheplygina, Felienne Hermans, Casper J. Albers, Natalia Z. Bielczyk, Ionica Smeets
2019	J	`jnl`	A Survey of Crowdsourcing in Medical Image Analysis. CoRR Silas Nyboe Ørting, Andrew Doyle, Matthias Hirth, Arno van Hilten, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina
2019	—	`ed.`	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 LABELS/HAL-MICCAI/CuRIOUS@MICCAI 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
2019	J	`jnl`	Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical Image Anal. Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim
2018	J	`jnl`	Cats or CAT scans: transfer learning from natural or medical image source datasets? CoRR Veronika Cheplygina
2018	J	`jnl`	Characterizing multiple instance datasets. CoRR Veronika Cheplygina, David M. J. Tax
2018	—	`conf`	Crowd Disagreement About Medical Images Is Informative. CVII-STENT/LABELS@MICCAI Veronika Cheplygina, Josien P. W. Pluim
2018	J	`jnl`	Crowd disagreement of medical images is informative. CoRR Veronika Cheplygina, Josien P. W. Pluim
2018	—	`conf`	Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphysema in Chest CT Scans. CVII-STENT/LABELS@MICCAI Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
2018	J	`jnl`	Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans. CoRR Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
2018	—	`ed.`	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 CVII-STENT/LABELS@MICCAI 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
2018	J	`jnl`	Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognit. Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon
2018	J	`jnl`	Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. CoRR Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim
2018	J	`jnl`	Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease. IEEE J. Biomed. Health Informatics Veronika Cheplygina, Isabel Pino Peña, Jesper Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne
2017	J	`jnl`	Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images. CoRR Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, Marleen de Bruijne
2017	J	`jnl`	Classification of COPD with Multiple Instance Learning. CoRR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne
2017	—	`conf`	Crowdsourced Emphysema Assessment. CVII-STENT/LABELS@MICCAI Silas Nyboe Ørting, Veronika Cheplygina, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
2017	J	`jnl`	Early Experiences with Crowdsourcing Airway Annotations in Chest CT. CoRR Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
2017	—	`conf`	Exploring the Similarity of Medical Imaging Classification Problems. CVII-STENT/LABELS@MICCAI Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dashtbozorg, Josien P. W. Pluim
2017	J	`jnl`	Exploring the similarity of medical imaging classification problems. CoRR Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dasht Bozorg, Josien P. W. Pluim
2017	—	`ed.`	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 CVII-STENT/LABELS@MICCAI 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
2017	J	`jnl`	Label Stability in Multiple Instance Learning. CoRR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
2017	J	`jnl`	Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners. CoRR Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne
2017	J	`jnl`	Transfer learning for multi-center classification of chronic obstructive pulmonary disease. CoRR Veronika Cheplygina, Isabel Pino Peña, Jesper Johannes Holst Pedersen, David A. Lynch, Lauge Sørensen, Marleen de Bruijne
2016	—	`conf`	Asymmetric similarity-weighted ensembles for image segmentation. ISBI Veronika Cheplygina, Annegreet van Opbroek, Mohammad Arfan Ikram, Meike W. Vernooij, Marleen de Bruijne
2016	J	`jnl`	Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. Veronika Cheplygina, David M. J. Tax, Marco Loog
2016	—	`conf`	Early Experiences with Crowdsourcing Airway Annotations in Chest CT. LABELS/DLMIA@MICCAI Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
2016	J	`jnl`	Multiple Instance Learning: A Survey of Problem Characteristics and Applications. CoRR Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon
2016	—	`conf`	The Similarity Between Dissimilarities. S+SSPR David M. J. Tax, Veronika Cheplygina, Robert P. W. Duin, Jan van de Poll
2015	—	`conf`	Characterizing Multiple Instance Datasets. SIMBAD Veronika Cheplygina, David M. J. Tax
2015	—	`conf`	Dissimilarity Representations for Low-Resolution Face Recognition. SIMBAD Mairelys Hernández-Durán, Veronika Cheplygina, Yenisel Plasencia Calana
2015	—	`—`	Dissimilarity-Based Multiple Instance Learning. Veronika Cheplygina
2015	—	`conf`	Label Stability in Multiple Instance Learning. MICCAI (1) Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
2015	J	`jnl`	Multiple instance learning with bag dissimilarities. Pattern Recognit. Veronika Cheplygina, David M. J. Tax, Marco Loog
2015	J	`jnl`	On classification with bags, groups and sets. Pattern Recognit. Lett. Veronika Cheplygina, David M. J. Tax, Marco Loog
2015	J	`jnl`	Single- vs. multiple-instance classification. Pattern Recognit. Ethem Alpaydin, Veronika Cheplygina, Marco Loog, David M. J. Tax
2014	B	`conf`	Classification of COPD with Multiple Instance Learning. ICPR Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Johannes Holst Pedersen, Marco Loog, Marleen de Bruijne
2014	J	`jnl`	Dissimilarity-based Ensembles for Multiple Instance Learning. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2014	—	`conf`	Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder. MLMI Veronika Cheplygina, David M. J. Tax, Marco Loog, Aasa Feragen
2014	J	`jnl`	On Classification with Bags, Groups and Sets. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2014	J	`jnl`	Quantile Representation for Indirect Immunofluorescence Image Classification. CoRR David M. J. Tax, Veronika Cheplygina, Marco Loog
2013	—	`conf`	Combining Instance Information to Classify Bags. MCS Veronika Cheplygina, David M. J. Tax, Marco Loog
2013	J	`jnl`	Multiple Instance Learning with Bag Dissimilarities. CoRR Veronika Cheplygina, David M. J. Tax, Marco Loog
2013	—	`conf`	On the Informativeness of Asymmetric Dissimilarities. SIMBAD Yenisel Plasencia Calana, Veronika Cheplygina, Robert P. W. Duin, Edel B. García Reyes, Mauricio Orozco-Alzate, David M. J. Tax, Marco Loog
2012	J	`jnl`	Bridging Structure and Feature Representations in Graph Matching. Int. J. Pattern Recognit. Artif. Intell. Wan-Jui Lee, Veronika Cheplygina, David M. J. Tax, Marco Loog, Robert P. W. Duin
2012	—	`conf`	Class-Dependent Dissimilarity Measures for Multiple Instance Learning. SSPR/SPR Veronika Cheplygina, David M. J. Tax, Marco Loog
2012	B	`conf`	Does one rotten apple spoil the whole barrel? ICPR Veronika Cheplygina, David M. J. Tax, Marco Loog
2011	—	`conf`	Bag Dissimilarities for Multiple Instance Learning. SIMBAD David M. J. Tax, Marco Loog, Robert P. W. Duin, Veronika Cheplygina, Wan-Jui Lee
2011	—	`conf`	Pruned Random Subspace Method for One-Class Classifiers. MCS Veronika Cheplygina, David M. J. Tax