DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

Ghoshal, Biraja; Hikmet, Feria; Pineau, Charles; Tucker, Allan; Lindskog, Cecilia

doi:10.1016/j.mcpro.2021.100140

Cited by 14 publications

(9 citation statements)

References 62 publications

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“…We also provide in Supplementary Appendix SB a demonstration of our method applied to IHC images and scRNA of testis. We benchmark against transcriptomic baselines as shown here, as well as DeepHistoClass ( Ghoshal et al , 2021 ), a supervised learning algorithm trained on images of testis that were manually labelled for cell-type specificity by human experts.…”

Section: Resultsmentioning

confidence: 99%

“…Supervised learning has been applied previously to the HPA IHC dataset, also for predicting subcellular localization ( Hu et al , 2022 ; Li et al , 2012 ; Long et al , 2020 ; Newberg and Murphy, 2008 ). Ghoshal et al (2021) train a Bayesian neural network to classify cell type specificity of proteins imaged in IHC of testis, for which they rely on a training set of images manually annotated with cell type labels. In contrast, here we demonstrate how embeddings of IHC images learned via self-supervision can be combined with independent single-cell transcriptomics to predict cell type specificity without the need for human labeling beforehand.…”

Section: Introductionmentioning

confidence: 99%

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Self-supervised learning of cell type specificity from immunohistochemical images

2022

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Motivation Advances in bioimaging now permit in situ proteomic characterization of cell–cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image. Results We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics. Availability and implementation Code and trained model are available at www.github.com/murphy17/HPA-SimCLR. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-supervised learning of cell type specificity from immunohistochemical images

2022

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“…Another deep learning-based method was developed to assess the stages of Wistar rat spermatogenic cycle on hematoxylin-eosin-stained digital slides, which makes the quick evaluation of stage-frequency possible [71]. Deep learning can be used in the classification of the immunohistochemistry images of human testis and improve the diagnostic performance [72].…”

Section: Discussionmentioning

confidence: 99%

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine

Sziva

Ács

Tőkés

et al. 2022

Life

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Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

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“…By involving international experts with extensive knowledge regarding certain human organs, the spatial data could be analyzed with a higher resolution. Available images of human testis served as a first pilot project to distinguish protein expression in eight different cell types instead of previously two (Ghoshal et al, 2021 ; Pineau et al, 2019 ). Due to the complex physiological processes taking place during spermatogenesis and the fact that testis harbors a large number of proteins not previously characterized, this organ is a priority for ongoing follow‐up studies related to the Tissue section (Green et al, 2018 ; Guo et al, 2018 ).…”

Section: Integrated Omics For Increasing the Single Cell Resolutionmentioning

confidence: 99%

The human protein atlas—Integrated omics for single cell mapping of the human proteome

Digre

Lindskog

2023

Protein Science

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Studying the spatial distribution of proteins provides the basis for understanding the biology, molecular repertoire, and architecture of every human cell. The Human Protein Atlas (HPA) has grown into one of the world's largest biological databases, and in the most recent version, a major update of the structure of the database was performed. The data has now been organized into 10 different comprehensive sections, each summarizing different aspects of the human proteome and the protein‐coding genes. In particular, large datasets with information on the single cell type level have been integrated, refining the tissue and cell type specificity and detailing the expression in cell states with an increased resolution. The multi‐modal data constitute an important resource for both basic and translational science, and hold promise for integration with novel emerging technologies at the protein and RNA level.

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DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

Cited by 14 publications

References 62 publications

Self-supervised learning of cell type specificity from immunohistochemical images

Self-supervised learning of cell type specificity from immunohistochemical images

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine

The human protein atlas—Integrated omics for single cell mapping of the human proteome

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