Titlebook: Computational Mathematics Modeling in Cancer Analysis; First International Wenjian Qin,Nazar Zaki,Fan Yang Conference proceedings 2022 The

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https://doi.org/10.1007/978-3-319-15446-6 at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of . at patch level, and an average accuracy of . at sample level, which is also verified in an independent cohort.

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,MLCN: Metric Learning Constrained Network for?Whole Slide Image Classification with?Bilinear Gated apture relations among sub-characteristics of tumor issues. Experiments on CAMELYON16 and TCGA Kidney datasets validate the effectiveness of our approach, and we achieved state-of-the-art performance compared to other popular methods. The codes will be available soon.

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,Cross-Stream Interactions: Segmentation of?Lung Adenocarcinoma Growth Patterns, at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of . at patch level, and an average accuracy of . at sample level, which is also verified in an independent cohort.

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