AI Article Synopsis

  • Developing automated quantitative image analysis pipelines requires careful planning to ensure consistent and meaningful data extraction.
  • Traditional methods rely on predefined rules for data extraction, but Machine/Deep Learning (ML/DL) can automate this process, enhancing tasks like segmentation and classification.
  • The text outlines essential terms, steps for creating effective segmentation pipelines, and important technical considerations for building reliable automated image analysis using ML/DL.

Article Abstract

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.

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Source
http://dx.doi.org/10.1007/978-1-0716-2051-9_20DOI Listing

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