Trefwoorden:
Alle publicaties voor Nataliia Molchanova
2026
| , , , , , , , , , , , , , , , , , , en , A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis (2026), in: NeuroImage: Clinical, 50(104007) |
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| , , , , , , , en , Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports, 16(6917) |
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2025
| , , , , en , Chapter 6 - Domain shift, domain adaptation, and generalization: A focus on MRI, Elsevier, 2025 |
| , , , , , , , , , , , , , , , en , Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors, 2025 |
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| , , , , , , en , Exploiting XAI maps to improve MS lesion segmentation and detection in MRI, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI, 2025 |
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| , , , , , , , , en , Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation (2025), in: Computers in Biology and Medicine |
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2024
| , , , , en , Fast refacing of MR images with a generative neural network lowers re‐identification risk and preserves volumetric consistency (2024), in: Human Brain Mapping, 45:9 |
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| , , , , , , , en , Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 |
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| , , , , , , , , , en , Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis, 2024 |
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| , , , , , , , en , Towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling, 2024 |
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2023
| , , , , , , , en , Deep learning uncertainty quantification of cortical lesions in MP2RAGE for missed lesions discovery, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023 |
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| , , , , , , , en , FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023 |
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| , , , , , , en , FLAWS against flaws: Improving Automated Cortical Lesion Segmentation, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , en , Identification of paramagnetic rim lesions using conventional MRI - a deep learning approach, in: 39th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2023 |
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| , , , , , , , en , NOVEL STRUCTURAL-SCALE UNCERTAINTY MEASURES AND ERROR RETENTION CURVES: APPLICATION TO MULTIPLE SCLEROSIS (2023), in: Proceedings of International Symposium of Biomedical Imaging 2023 |
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| , , , , , , , , , , , , , en , Streamline RimNet: A Deep Learning Classification of Paramagnetic Rim Lesions, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023 |
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| , , , , , en , Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation, in: IEEE International Symposium on Biomedical Imaging, 2023 |
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| , , , , , , , , en , The Normalised Dice Similarity Coefficient for MS: tackling lesion load biases in white matter and cortical lesion segmentation, European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Conference 2023, 2023 |
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| , , , , , , , en , Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis, ISMRM & ISMRT 2023 Annual Meeting & Exhibition, 2023 |
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| , , , , , , , en , Towards Informative Uncertainty Measures for MRI Segmentation in Clinical Practice: Application to Multiple Sclerosis, in: Bern Interpretable Symposium (BIAS) 2023, 2023 |
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2022
| , , , , , , , , , , , , , , , , en , Shifts 2.0: Extending The Dataset of Real Distributional Shifts, arXiv, 2022 |
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