Multi-Modal
… ML for tomography
Applications
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convexAdam: Fast and accurate optimisation for registration with little learning
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histology: pseudo-histology synthesis training from corresponding micro-CT slices on manually registered samples
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Active Learning with HRNet: Deep High-Resolution Representation Learning for Visual Recognition
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SAM, SAM++ and Cross-SAM: match arbitrary anatomical landmarks between two radiological images (e.g. CT, MR, X-ray, etc.)
Publications
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Artificial intelligence for synchrotron-radiation tomography: Julian P. Moosmann et al., Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265502, doi: 10.1117/12.2675628
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CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation: Reuben Dorent et al., Medical Image Analysis, doi: 10.1016/j.media.2022.102628
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Multi-modal Unsupervised Domain Adaptation for Deformable Registration Based on Maximum Classifier Discrepancy: Christian N. Kruse et al., Bildverarbeitung für die Medizin, doi: 10.1007/978-3-658-33198-6_47
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Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Mover’s discrepancies: M.P.Heinrich et al., , doi: