MedGIFT
Thema: Hasler MSXplain 21042
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Publikationen für Thema: Hasler MSXplain 21042 sortiert nach Aktualität
| , , , , , , , , , , , , und , A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis (2026), in: Multiple Sclerosis Journal |
| , , , , , , , , , , und , Deep-PRL: a deep learning network for the identification of paramagnetic rim lesions in multiple sclerosis, in: ISMRM 2025, 2025 |
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| , , , , , , und , 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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| , , , , , , , und , Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports |
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| , , , , und , A Bispectral 3D UNet for Rotation Robustness in Medical Segmentation, in: The First Workshop on Topology- and Graph-Informed Imaging Informatics at MICCAI, Seiten 43-54, Springer Nature Switzerland, 2024 |
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| , , , , , , und , The value of AI for assessing longitudinal brain metastases treatment response (2025), in: Neuro-Oncology Advances, 7:1 |
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| , , , , , , , und , Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , 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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, , , , , , und , How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review (2023), in: NeuroImage: Clinical, 39(103491)
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| , , , , , , , und , 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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| , , , , , , , , , und , Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction (2024), in: Nature Scientific Reports, 14:9644 |
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, , , , , , , , , , und , Automatic Head and Neck Tumor Segmentation and Outcome Prediction Relying on FDG-PET/CT Images: Findings from the Second Edition of the HECKTOR Challenge (2023), in: Medical Image Analysis, 90:1(102972)
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| , , , , , , , , , , , , , , und , A Global Taxonomy of Interpretable AI: Unifying the Terminology for the Technical and Social Sciences (2022), in: Artificial Intelligence Review, 56(3473–3504) |
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| , , , , , , , und , HEad and neCK TumOR segmentation and outcome prediction: The HECKTOR challenge, in: European Society of Radiology, 2022 |
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| , , , , und , Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT, in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Seiten 4731-4735, 2022 |
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, , , , , , , , und , QuantImage v2: A Comprehensive and Integrated Physician-Centered Cloud Platform for Radiomics and Machine Learning Research (2023), in: European Radiology Experimental, 7:16
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