Interactive phenotyping of large-scale histology imaging data with HistomicsML

Nalisnik, Michael; Amgad, Mohamed; Lee, Sanghoon; Halani, Sameer H.; Vega, José E. Velázquez; Brat, Daniel J.; Gutman, David; Cooper, Lee

doi:10.1038/s41598-017-15092-3

Cited by 49 publications

(19 citation statements)

References 41 publications

(29 reference statements)

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“…The next step was image annotation for supervised machine learning. Although web-based histopathological annotation tools exist 51,52 , we developed a simple platform using the cloud-based Amazon Web Services Elastic Beanstalk 39 infrastructure (Supplementary Fig. 3) for study design flexibility and for the speed of its keystroke-based entry format.…”

Section: Discussionmentioning

confidence: 99%

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

et al. 2019

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Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping.

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Section: Discussionmentioning

confidence: 99%

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

et al. 2019

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“…Digital pathology is emerging as an increasingly important facet of the approach to glioma pathology and classification and has been employed in both ML and DL approaches to integrate information from both histology images and genomic biomarkers to predict time-to-event outcomes (36). It has been utilized for wholeslide imaging of histologic sections to extract quantitative features (90). Powell et al used hematoxylin-and eosin-stained slides from TCGA to create a machine learned dictionary of "image-derived visual words" associated with survival outcomes while connecting image-derived phenotypic characteristics with molecular signaling activity and the behavior of low-grade glioma (91).…”

Section: Molecular and Genetic Characterization Of Glioma Digital Pathology And Survival Predictionmentioning

confidence: 99%

Survival Prediction in Gliomas: Current State and Novel Approaches

Zhao

Krauze

2021

Gliomas

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Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.

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“…explored the use of synthetic data to produce nuclear segmentations [ 41 ]. While a significant contribution, their work did not address classification, relied on qualitative slide-level evaluations of results, and did not explore how algorithmic bias affects data quality [ 22 , 42 ]. The approach we used involves click-based approval of annotations generated by a deep-learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

Amgad

Atteya²,

Hussein

et al. 2022

GigaScience

Self Cite

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Background Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. Results This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. Conclusions This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

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Interactive phenotyping of large-scale histology imaging data with HistomicsML

Cited by 49 publications

References 41 publications

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Survival Prediction in Gliomas: Current State and Novel Approaches

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer

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