A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association

Zarella, Mark D.; Bowman, Douglas; Aeffner, Famke; Farahani, Navid; Xthona, Albert; Absar, Syeda Fatima; Parwani, Anil V.; Bui, Marilyn M.; Hartman, Douglas J.

doi:10.5858/arpa.2018-0343-ra

Cited by 264 publications

(232 citation statements)

References 89 publications

Supporting

Mentioning

228

Contrasting

Unclassified

Order By: Relevance

“…Histological analyses of tumor biopsy sections have been important tools in oncology for more than a century, providing a high-resolution map of the tumor that helps pathologists to determine both diagnosis and grade 1,2 . The development of new, more powerful technologies, and the curation of larger datasets, have made it possible to train increasingly sophisticated algorithms, which can to process and learn from very high-definition whole-slide digital images (WSI).…”

mentioning

confidence: 99%

Transcriptomic learning for digital pathology

Schmauch¹,

Romagnoni²,

Pronier³

et al. 2019

Preprint

View full text Add to dashboard Cite

^,⋕ : These authors contributed equally.Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and the prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides, has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that our deep learning model HE2RNA can be trained to predict systematically RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. The model facilitates the virtual spatialization of gene expression, as validated by double-staining in an independent dataset. The results can therefore be interpreted in detail and this model opens up new opportunities for virtual staining. Finally, the transcriptomic representation learned by the model could be could be used to improve performances for other clinical tasks, particularly for small datasets. For example we studied the problem of predicting microsatellite instability from Hematoxylin & Eosin (H&E)-stained images. Greater prediction ability was achieved in such a realistic framework.

show abstract

mentioning

confidence: 99%

Transcriptomic learning for digital pathology

Schmauch¹,

Romagnoni²,

Pronier³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Technological advances now allow WSI to be relatively fast and the images have high resolution. For example, to facilitate automation, new generations of slide scanners have incorporated tissue identification abilities, allowing the scanner to localize the tissue on the slide, and/or auto-focusing methods (14). Moreover, WSI has proved particularly suitable for applying deep learning algorithms that can assist human analysis of digital images of tissues (10,15).…”

Section: Whole Slide Imaging Technologymentioning

confidence: 99%

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

et al. 2020

View full text Add to dashboard Cite

Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra-and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

show abstract

“…. , 9). This allows variance to be 376 estimated for Random Forest learning, but methods based exclusively on the L1 norm 377 are fully deterministic, so these have zero estimated variance (Table S1).…”

Section: Distinguishing Ten Histopathology Tissue Types 324mentioning

confidence: 99%

“…Procedure overview in the supplement explains further (Sec S5.4). many images from whole slide scanners, which at a global scale have been adopted 20 slowly, due in part to cost and complexities of digital pathology workflows [8,9]. 21 For machine learning to work accurately, it must be trained on a sufficiently large 22 dataset.…”

mentioning

confidence: 99%

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Schaumberg

Juarez-Nicanor

Choudhury

et al. 2018

Preprint

View full text Add to dashboard Cite

Large-scale annotated image datasets like ImageNet and CIFAR-10 have been essential in developing and testing sophisticated new machine learning algorithms for natural vision tasks. Such datasets allow the development of neural networks to make visual discriminations that are done by humans in everyday * Respective contributions. 1. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/396663 doi: bioRxiv preprint first posted online Aug. 21, 2018; activities, e.g. discriminating classes of vehicles. An emerging field -computational pathology -applies such machine learning algorithms to the highly specialized vision task of diagnosing cancer or other diseases from pathology images. Importantly, labeling pathology images requires pathologists who have had decades of training, but due to the demands on pathologists' time (e.g. clinical service) obtaining a large annotated dataset of pathology images for supervised learning is difficult. To facilitate advances in computational pathology, on a scale similar to advances obtained in natural vision tasks using ImageNet, we leverage the power of social media. Pathologists worldwide share annotated pathology images on Twitter, which together provide thousands of diverse pathology images spanning many sub-disciplines. From Twitter, we assembled a dataset of 2,746 images from 1,576 tweets from 13 pathologists from 8 countries; each message includes both images and text commentary. To demonstrate the utility of these data for computational pathology, we apply machine learning to our new dataset to test whether we can accurately identify different stains and discriminate between different tissues. Using a Random Forest, we report (i) 0.959 ± 0.013 Area Under Receiver Operating Characteristic [AUROC] when identifying single-panel human hematoxylin and eosin [H&E] stained slides that are not overdrawn and (ii) 0.996 ± 0.004 AUROC when distinguishing H&E from immunohistochemistry [IHC] stained microscopy images. Moreover, we distinguish all pairs of breast, dermatological, gastrointestinal, genitourinary, and gynecological [gyn] pathology tissue types, with mean AUROC for any pairwise comparison ranging from 0.771 to 0.879. This range is 0.815 to 0.879 if gyn is excluded. We report 0.815 ± 0.054 AUROC when all five tissue types are considered in a single multiclass classification task. Our goal is to make this large-scale annotated dataset publicly available for researchers worldwide to develop, test, and compare their machine learning methods, an important step to advancing the field of computational pathology.

show abstract

A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association

Cited by 264 publications

References 89 publications

Transcriptomic learning for digital pathology

Transcriptomic learning for digital pathology

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Contact Info

Product

Resources

About