![]() It allows researchers, even those without computer vision or machine-learning knowledge, to efficiently characterize and exploit their cell-based and image-based HCS experiments, leading to new discoveries. #Cellprofiler 2.2.0 softwareFurthermore, these procedures make it difficult to discover new phenotypes or to intelligently refine decision boundaries (ĪCC v2.0 is a completely re-designed and user-friendly software tool with the goal of improving the collection and understanding of image data and the accuracy of the analysis ( Figure S1). These methods are inefficient and generate many wasteful annotations that ultimately do not prove useful for the classifier. Existing software packages force the user to manually select cells to label or randomly select cells. There are also limitations in the annotation process. Experts are often unsure if they have uncovered all the important phenotypes buried within the data because they lack the tools to fully explore it. The cost of collecting expert annotations is high and categorizing cells into strict classes is often ambiguous. Little attention has been given to the methods used to explore the data, understand it efficiently, or to ensure the quality of the annotations. ![]() ) it lacked discovery and data visualization tools to enable the user to fully explore and understand their data. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at Graphical Abstract We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. ![]() ![]() Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. #Cellprofiler 2.2.0 manualHigh-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |