Automated Zebrafish Phenotype Pattern Recognition: 6 Years Ago, and Now.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-12-01 DOI:10.1089/zeb.2022.0027

Mark Schutera, Luca Rettenberger, Markus Reischl

引用次数: 0

Abstract

The article assesses the developments in automated phenotype pattern recognition: Potential spikes in classification performance, even when facing the common small-scale biomedical data set, and as a reader, you will find out about changes in the development effort and complexity for researchers and practitioners. After reading, you will be aware of the benefits and unreasonable effectiveness and ease of use of an automated end-to-end deep learning pipeline for classification tasks of biomedical perception systems.

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自动斑马鱼表型模式识别:6年前和现在。

本文评估了自动表型模式识别的发展:分类性能的潜在峰值，即使面对常见的小规模生物医学数据集，作为读者，您将发现研究人员和实践者在开发工作和复杂性方面的变化。阅读后，您将意识到用于生物医学感知系统分类任务的自动化端到端深度学习管道的好处和不合理的有效性和易用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.