Titlebook: Computational Mathematics Modeling in Cancer Analysis; Second International Wenjian Qin,Nazar Zaki,Chao Li Conference proceedings 2023 The

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https://doi.org/10.1007/BFb0030514for early screening of hepatocellular carcinoma (HCC). However, the complex morphology and wide variations of liver and tumors in MRI images may not be fully captured by relying solely on pixel-level information. Therefore, combining shape-aware information becomes critical, as it provides additiona

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https://doi.org/10.1007/978-1-940033-37-2ditionally done manually by oncologists, a process that is time-consuming and subject to individual subjectivity. Although fully automated deep-learning models could offer a solution, their segmentation performance is often hindered by the lack of abundant annotated data. We retrospectively analyzed

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Studies of Vortex Dominated Flowsration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. We desire an efficient solution to jointly account for spatial deformations and appearance changes in the pathological regions whe

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Conference proceedings 2023n October 8, 2023, in Vancouver, BC, Canada.??..The 17 full papers presented were carefully reviewed and selected from 25 submissions.?The conference focuses on?the discovery of cutting-edge techniques addressing trends and challenges in theoretical, computational, and applied aspects of mathematical cancer data analysis.. . . .

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Computational Mathematics Modeling in Cancer Analysis978-3-031-45087-7Series ISSN 0302-9743 Series E-ISSN 1611-3349