A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Peikari, Mohammad; Salama, Sherine; Nofech‐Mozes, Sharon; Martel, Anne L.

doi:10.1038/s41598-018-24876-0

Cited by 128 publications

(68 citation statements)

References 29 publications

(12 reference statements)

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“…These approaches still employ manual annotations but make use of raw unlabeled data to lower the necessary amount of labeled data 24 . Only a few previous works [25][26][27] have used semi-supervised learning for digital pathology image analysis. Peikari et al recently introduced a cluster-then-label method based on support vector machine classifier that is shown to outperform classical supervised classifiers 25 .…”

Section: Introductionmentioning

confidence: 99%

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

Kapil¹,

Meier²,

Żuraw³

et al. 2018

Sci Rep

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The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

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Section: Introductionmentioning

confidence: 99%

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

Kapil¹,

Meier²,

Żuraw³

et al. 2018

Sci Rep

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“…SOM is an unsupervised learning technique in which unlabeled dataset are grouped into clusters that share similar properties. Unlike supervised learning methods with labeled information as training data, there is no clear way to validate the quality of the unsupervised approach [31].…”

Section: The Pso-som Methodsmentioning

confidence: 99%

A Hybrid Swarm Intelligence Approach for Blog Success Prediction

Hsu¹,

Wu²,

Chiu³

2019

IJCIS

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A B S T R A C TSuccessful blogs receive high ratings and generate marketing value. What factors contribute to the success of a blog and how to predict its success level are questions worth discussing. A hybrid swam intelligence approach is proposed in this study to predict blog success level. First, this study develops a research model of blog success with six factors from content, technology, and social views of point, which include currentness, design, reliability, security, interaction, and connectivity. A questionnaire is designed based on the blog success model. Two hundred ten valid samples are collected from Internet users with experience in using or creating blogs. A hybrid approach combining particle swarm optimization (PSO) and self-organizing map (SOM) is proposed to predict blog success level. The results of 10-fold validation are examined to compare the hybrid PSO-SOM approach with the results from three classifiers: C5.0, classification and regression trees (CARTs), and support vector machine (SVM). For blog success prediction, the results indicate the PSO-SOM approach demonstrates higher accuracy among these methods.

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“…Ensembles of support vector machines (SVMs) were used by Manivannan et al [30] to detect and classify cellular patterns. Peikari et al [28,31] designed an analysis pipeline where a clustering operation is executed on input data to detect the structure of the data space, where a semi-supervised learning method is then executed to carry out classification using clustering information. Chen et al [32] developed a deep learning framework for segmentation that implemented a multi-task learning approach by the use of multi-level CNNs.…”

Section: Image Analysis Tasks and Machine Learningmentioning

confidence: 99%

The Emergence of Pathomics

et al. 2019

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Purpose of Review Our goal is to provide an overview of machine learning methods and artificial intelligence in digital pathology image analysis. We also highlight novel visualization tools to interpret quantitative image-based pathomics data that is extracted from whole slide images to describe diverse phenotypic characteristics of cancer in a spectrum of tissues. Recent Findings Image analysis of tissues is based on the identification and classification of tissue, architectural elements, cells, nuclei, and other histologic features. We report emerging digital pathology image analysis applications to study several types and subtypes of cancer to complement traditional histopathologic evaluation. Summary WSIs typically contain hundreds of thousands to millions of objects within a heterogeneous histologic landscape. Therefore, Pathomics represents an incredibly powerful emerging approach to classify cellular interactions and signaling by identifying relevant spatial relationships. The quantification of the intrinsic variability of different phenotypes and behavior in cancer is useful in analyzing and predicting clinical outcomes and treatment response.

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A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Cited by 128 publications

References 29 publications

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

A Hybrid Swarm Intelligence Approach for Blog Success Prediction

The Emergence of Pathomics

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