Enhanced CellClassifier: a multi-class classification tool for microscopy images

Misselwitz, Benjamin; Strittmatter, Gerhard; Periaswamy, Balamurugan; Schlumberger, Markus C.; Samuel, Rout; Horváth, Péter; Kozak, Karol; Hardt, Wolf‐Dietrich

doi:10.1186/1471-2105-11-30

Cited by 83 publications

(61 citation statements)

References 35 publications

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“…A few studies have made use of unsupervised learning techniques such as clustering 18 and self-organizing maps, 19 but the majority have opted for supervised techniques. 4,5,7,8 SML infers a mapping function from labeled training data, which consists of a set of training example pairs (a feature vector describing the cell and the desired class label). The supervised learning algorithm uses the training data to produce a function that maps unseen instances to the desired output.…”

Section: Multiparametric Analysis Using Smlmentioning

confidence: 99%

Active Learning Strategies for Phenotypic Profiling of High-Content Screens

Smith

Horváth

2014

SLAS Discovery

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High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied.

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Section: Multiparametric Analysis Using Smlmentioning

confidence: 99%

Active Learning Strategies for Phenotypic Profiling of High-Content Screens

Smith

Horváth

2014

SLAS Discovery

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“…Using such approaches, it is possible to label many different objects in the image and assign them to one of several possible phenotypes. Freely available programs that are able to perform this feature, such as Enhanced Cell Classifier, allow rapid analysis of complex phenotypes (Misselwitz et al, 2010). An additional analysis software program that is available upon request is CalMorph, a highthroughput image-processing program that was specifically developed for morphological analysis of the yeast Saccharomyces cerevisiae (Negishi et al, 2009;Ohtani et al, 2004).…”

Section: Analysis Softwarementioning

confidence: 99%

Getting the whole picture: combining throughput with content in microscopy

Rimon

Schuldiner

2011

Journal of Cell Science

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Commentary 3743Introduction Technological developments in the past years have led to the emergence of high-throughput methods in cell biology (Box 1), where multiple perturbations of a system are automatically repeated in a controlled and identical fashion. These capabilities allow the acquisition of vast amounts of data on a biological system. Evaluating all aspects of such data using computational and mathematical tools can then lead to an unbiased systematic representation of the process studied (Ahn et al., 2006). Technical breakthroughs, often driven by the pharmaceutical industry, have pushed this field forwards and have led to major advances in drug screening (reviewed by Zanella et al., 2010) and numerous discoveries in basic biology.The need for systematic and unbiased approaches has always been at the core of scientific efforts. However, the technology that is required for such efforts often displays an inverse relationship between throughput (speed) and content (biological information). Sydney Brenner referred to this phenomenon by saying that highthroughput experiments are in danger of creating "low-input, high-throughput, no-output biology" (Brenner, 2008). Therefore, it remains a major goal to develop high-throughput science that will give all the advantages of being systematic, accurate, fast and unbiased without giving up the requirement to provide profound and highly informative data.Among the variety of high-throughput approaches available to date (Box 1), one of the methods that holds the promise to bridge the gap between these expectations is high-throughput microscopy, often also referred to as high-content screening (HCS). This is mainly owing to the ability of microscopy methods to focus on single cells at a subcellular resolution, in a time-dependent manner and to measure a large number of parameters in each frame. In this Commentary, we focus on the current frontiers of HCS by presenting the wide range of tools that are utilized and the biological questions that can be tackled using such approaches. We also discuss possible ways to increase content while maintaining throughput at all levels of the screen -from sample design and preparation through to the acquisition and analysis capabilities of the high-content imager system. The power of high-throughput microscopyHigh-throughput microscopy can be employed to address a wide spectrum of biological questions. At the basis of this capability lies the ever-growing variety of labeling methods (discussed below) that allow visualization of cellular architecture and function, as well as developmental or behavioral processes (Fig. 1). In addition, the technological advance of biological tools and microscopic platforms now enables screens to be performed in an array of genetic backgrounds, under different growth conditions and at various time points, as well as allowing the comparison of multiple tissues, cell lines or organisms. Combining the richness of visualization approaches with various experimental strategies creates nearly endless options ...

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“…This application was tested using the benchmark data sets from the IICBU Biological Image Repository [13]. Other open source applications for the research purpose in this category include CellProfiler [14] which focuses on cell image classification, and recently a support vector machine based Enhanced CellClassifier [15] is made available for the research community.…”

Section: Introdutionmentioning

confidence: 99%

Histology Image Classification Using Supervised Classification and Multimodal Fusion

Meng

Lin

Shyu

et al. 2010

2010 IEEE International Symposium on Multimedia

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Abstract-The fast development of microscopy imaging techniques nowadays promotes the generation of a large amount of data. These data are very crucial not only for theoretical biomedical research but also for clinical usage. In order to decrease the inter-intra observer variability and save the human effort on labeling and classifying these images, a lot of research efforts have been devoted to the development of algorithms for biomedical images. Among such efforts, histology image classification is one of the most important areas due to its broad applications in pathological diagnosis such as cancer diagnosis. To improve classification accuracy, most of the previous work focuses on extracting more features and building algorithms for a specific task. This paper proposes a framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification. In the proposed framework, a cell image is first divided into 25 blocks to reduce the spatial complexity of computation, and one C-RSPM model is built on each block set which contains blocks in the same location from different images. For each testing image, our proposed framework first classifies each of its blocks using the C-RSPM classification model built for that block set, and then applies a multimodal late fusion algorithm with a weighted majority voting strategy to decide the final class label of the whole image. Experimenting using three-fold cross validation with three benchmark histology data sets shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.

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Enhanced CellClassifier: a multi-class classification tool for microscopy images

Cited by 83 publications

References 35 publications

Active Learning Strategies for Phenotypic Profiling of High-Content Screens

Active Learning Strategies for Phenotypic Profiling of High-Content Screens

Getting the whole picture: combining throughput with content in microscopy

Histology Image Classification Using Supervised Classification and Multimodal Fusion

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