Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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https://doi.org/10.1007/978-3-662-11735-4ntial for medical health. Most of the existing methods rely heavily on manually engineered domain knowledge, therefore, lack generalization and are inefficient. Drugs with similar molecular structures have similar chemical properties. The molecular structures of drugs can be obtained easily and the

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膽小鬼

https://doi.org/10.1007/978-3-662-11735-4f up to 0.78 without additional clinical information. Furthermore, the importance of the domain gap between two different image sources is considered, as it is important to create usability independent of hardware components such as the high-resolution scanner used. Since for the application of mach

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https://doi.org/10.1007/978-3-662-07208-0ous quantities of data need to be crunched in order to get the valuable parts and discard redundant ones. For those data represented as two-dimensional digital matrix, two alternative schemes by scanning the elements of the matrix in zigzag route have been proposed. Fixed-point mode and navigation m

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,Die elastizit?tstheoretischen Grundlagen, of manual pixel-level annotations. Existing methods relying on class activation map (CAM) to localize target objects suffer from two problems. First, most CAM-based models adopt convolutional neural networks, which cannot model the long-range dependencies of dispersed tissues. Second, CAM tends to

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