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Disambiguation of "Cheplygina, Veronika"

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

, , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 158-167. PMLR, (2021)

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To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Veronika Cheplygina

Veronika Schleper

Modeling, Analysis and Optimal Control of Gas Pipeline Networks. TU Kaiserslautern, (2009)

Veronika Kraft

Veronika Mayer

Veronika Kneip

 

Other publications of authors with the same name

Crowdsourcing Airway Annotations in Chest Computed Tomography Images., , , , and . CoRR, (2020)Multi-task Learning with Crowdsourced Features Improves Skin Lesion Diagnosis., , , and . CoRR, (2020)Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning., and . iMIMIC/MIL3ID/LABELS@MICCAI, volume 12446 of Lecture Notes in Computer Science, page 242-253. Springer, (2020)Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging., , , , , and . CoRR, (2024)Detection of Furigana Text in Images., , and . CoRR, (2022)Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI?, , , , , , , , , and 12 other author(s). MICCAI (10), volume 15010 of Lecture Notes in Computer Science, page 124-132. Springer, (2024)Why is the Winner the Best?, , , , , , , , , and 115 other author(s). CVPR, page 19955-19966. IEEE, (2023)How I failed machine learning in medical imaging -- shortcomings and recommendations, and . (2021)cite arxiv:2103.10292.ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification., , , , and . CoRR, (2021)Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing., , , , , , , , , and 9 other author(s). iMIMIC/MIL3ID/LABELS@MICCAI, volume 12446 of Lecture Notes in Computer Science, page 1. Springer, (2020)