Processing 是一门开源编程语言和与之配套的集成开发环境(IDE)的名称。Processing 在电子艺术和视觉设计社区被用来教授编程基础,并运用于大量的新媒体和互动艺术作品中。

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论文题目: Learning Conceptual-Contextual Embeddings for Medical Text

论文摘要:

对于自然语言理解任务来说,外部知识通常是有用的。本文介绍了一个上下文文本表示模型,称为概念上下文(CC)嵌入,它将结构化的知识合并到文本表示中。与实体嵌入方法不同,文中提到的方法将知识图编码到上下文模型中。就像预先训练好的语言模型一样,CC嵌入可以很容易地在广泛的任务中重用。模型利用语义泛化,有效地编码了庞大的UMLS数据库。电子实验健康记录(EHRs)和医疗文本处理基准表明,而使得模型大大提高了监督医疗NLP任务的性能。

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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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