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Cell Reports MethodsBioinformaticsPeer-reviewed journal article

MIA: An Open Source Standalone Deep Learning Application for Microscopic Image Analysis

Nils KörberiD

PDFDOIPreprintCode

Abstract

AO_SCPLOWBSTRACTC_SCPLOWIn recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the Microscopic Image Analyzer (MIA). MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and is compatible with commonly used open source software packages. The software provides a unified interface for easy image labeling, model training and inference. Furthermore the software was evaluated in a public competition and performed among the top three for all tested data sets. The source code is available on https://github.com/MIAnalyzer/MIA.

Citations
21
FWCI
2.30
Field percentile
top 11%

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