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Update milda-poceviciute.md
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michellestegem authored Jul 9, 2024
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type: postdoc
email: m.poceviciute@gmail.com

Milda Pocevičiūtė is a postdoctoral researcher at the Computational Pathology Group of the Department of Pathology of Radboud University Medical Center in Nijmegen, The Netherlands. She holds a master of science degree in Statistics and Machine Learning from Linköping University in Sweden. In 2023, she obtained a PhD degree at the Center for Medical Image Science and Visualization (CMIV) at Linköping University. Her doctoral research focused on understanding and improving the robustness and reliability of deep learning systems tailored for digital pathology applications. She is particularly interested in topics of domain generalization, domain adaptation, and uncertainty estimation in digital pathology. In her postdoctoral work at the Computational Pathology Group, she is investigating ways to adapt a commercially available software solution for mitosis counting to new organs and tissues. Such approaches could highly reduce the costs of developing new commercial solutions for pathology laboratories. This postdoctoral work is in collaboration with Aiosyn software company.
Milda Pocevičiūtė is a postdoctoral researcher at the Computational Pathology Group of the Department of Pathology of Radboud University Medical Center in Nijmegen, The Netherlands. She holds a master of science degree in Statistics and Machine Learning from Linköping University in Sweden. In 2023, she obtained a PhD degree at the Center for Medical Image Science and Visualization (CMIV) at Linköping University. Her doctoral research focused on understanding and improving the robustness and reliability of deep learning systems tailored for digital pathology applications. She is particularly interested in topics of domain generalization, domain adaptation, and uncertainty estimation in digital pathology. In her postdoctoral work at the Computational Pathology Group she is working with [member/jeroen-van-der-laak], she is investigating ways to adapt a commercially available software solution for mitosis counting to new organs and tissues. Such approaches could highly reduce the costs of developing new commercial solutions for pathology laboratories. This postdoctoral work is in collaboration with Aiosyn software company.

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