Abstract
BackgroundDeep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application.ResultsWe have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented.ConclusionsWith InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.
Highlights
Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms
In the following we describe the algorithms implemented in InstantDL to address these tasks and how they can be applied within the pipeline
With InstantDL we provide 10 pre-trained weight sets: four pre-trained weights from 2D nuclei segmentation, two pre-trained weights from 2D lung segmentation [16], two from 3D in-silico staining [7], and one from the classification of white blood cells [17, 18] and metastatic cancer [19], respectively
Summary
Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Deep learning has revolutionised image processing [1]. For specific biomedical image analysis tasks such as cell segmentation [2, 3], cell classification [4,5,6] or in-silico staining [7, 8], deep learning algorithms achieve higher accuracy than trained experts [6, 9, 10] and outperform humans at data processing speed and prediction consistency [11, 12]. We here provide InstantDL, a pipeline that automates pre- and post-processing for biomedical deep learning applications.
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