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数据可视化
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https://www.oreilly.com/library/view/fundamentals-of-data/9781492031079/
https://clauswilke.com/dataviz/
在自然科学和社会科学领域中，有效的可视化是沟通来自日益庞大和复杂的数据集的信息的最佳方式。但是，随着可视化软件的日益强大，科学家、工程师和业务分析人员经常不得不在一组令人困惑的可视化选择和选项中导航。
这本实用的书带您通过许多常见的可视化问题，并提供了如何将大型数据集转化为清晰和引人注目的数字的指导方针。什么样的可视化类型最适合你想要讲述的故事?你如何制作视觉上令人愉悦的信息图表?作者Claus O. Wilke将向您介绍成功数据可视化最关键的元素。
探索颜色的基本概念，作为一种工具来突出，区分，或代表一种价值 理解冗余编码的重要性，以确保您以多种方式提供关键信息 使用这本书的可视化目录，一个常用数据可视化类型的图形指南 获得大量好的和坏的数据 学习如何在文档或报告中使用数字，以及如何有效地使用它们来讲述一个引人注目的故事
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Fundamentals of Data Visualization
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Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of the procedure allows pathologists to rapidly assess the key microscopic features, such as tumour margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time-critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists.
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