Python是一種麵向對象的解釋型計算機程序設計語言,在設計中注重代碼的可讀性,同時也是一種功能強大的通用型語言。

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當你正在匆忙編寫代碼並且需要一個答案時,你可以閱讀這本書。它是對核心語言的一個易於使用的引用,包括對常用模塊和工具包的描述,以及關於最近的變化、新特性和升級的內置組件的指南——所有這些更新都涵蓋了Python 3。X和版本2.6。您還可以通過方便的索引快速找到所需的內容。

由Mark Lutz編寫——被廣泛認為是世界領先的Python培訓師——Python Pocket Reference,第四版,是O'Reilly的經典Python教程的完美夥伴,也由Mark: Learning Python and Programming Python編寫。

內置對象類型,包括數字、列表、字典等 用於創建和處理對象的語句和語法 用於構造和重用代碼的函數和模塊 Python的麵向對象編程工具 異常處理模型 內置函數、異常和屬性 特殊的操作符重載方法 廣泛使用的標準庫模塊和擴展 命令行選項和開發工具 Python的習慣用法和提示

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Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues specific to the domain of medical image analysis, such as 3-D data handling and distance metrics for evaluation. pymia, an open-source Python package, tries to address these issues by providing flexible data handling and evaluation independent of the deep learning framework. Methods: The pymia package provides data handling and evaluation functionalities. The data handling allows flexible medical image handling in every commonly used format (e.g., 2-D, 2.5-D, and 3-D; full- or patch-wise). Even data beyond images like demographics or clinical reports can easily be integrated into deep learning pipelines. The evaluation allows stand-alone result calculation and reporting, as well as performance monitoring during training using a vast amount of domain-specific metrics for segmentation, reconstruction, and regression. Results: The pymia package is highly flexible, allows for fast prototyping, and reduces the burden of implementing data handling routines and evaluation methods. While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines. The developed package was successfully used in a variety of research projects for segmentation, reconstruction, and regression. Conclusions: The pymia package fills the gap of current deep learning frameworks regarding data handling and evaluation in medical image analysis. It is available at https://github.com/rundherum/pymia and can directly be installed from the Python Package Index using pip install pymia.

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