Nadezhda Koriakina
Postdoctoral position at Department of Materials Science and Engineering; Biomedical Engineering
- E-mail:
- nadezhda.koriakina@angstrom.uu.se
- Visiting address:
- Ångströmlaboratoriet, Lägerhyddsvägen 1
- Postal address:
- Box 35
751 03 UPPSALA
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Short presentation
Nadezhda joined the EMBLA research group - Enabling Microtechnologies for Biomedical and Life science Applications - in November 2024. Her research is focused on the analysis of data coming from organs-on-chip devices.
Keywords
- data driven life science
- machine learning
- precision medicine
Biography
2024-, PostDoc in EMBLA group, Uppsala University
2024, PhD in Computerized Image Processing, Uppsala University
2016-2018, Postgraduate Studies in Advanced Medical Imaging, KU Leuven, Belgium
2015, MSc in Micro and Nano Systems Technology, Buskerud and Vestfold University College, NorwayResearch
The interdisciplinary research project Nadezhda is involved in is aimed at exploring how machine learning and the combination of different types of data can be utilised to improve decisions during ongoing experiments on organs-on-chip as well as its potential for identifying new biological information.
Publications
Recent publications
- Deep Learning and Explainable Artificial Intelligence for Biomedical Applications (2024)
- Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection (2024)
- End-to-end Multiple Instance Learning with Gradient Accumulation (2022)
- The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning (2021)
- Uncovering hidden reasoning of convolutional neural networks in biomedical image classification by using attribution methods (2020)
All publications
Articles
- Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection (2024)
- Sensitive key instance detection: Helping a cytotechnologist to find a needle in a haystack
Books
Conferences
- End-to-end Multiple Instance Learning with Gradient Accumulation (2022)
- The Effect of Within-Bag Sampling on End-to-End Multiple Instance Learning (2021)
- Uncovering hidden reasoning of convolutional neural networks in biomedical image classification by using attribution methods (2020)
- Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes (2019)
