DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning

Şenaras, Çağlar; Niazi, M. Khalid Khan; Lozanski, Gerard; Gürcan, Metin N.

doi:10.1371/journal.pone.0205387

Cited by 97 publications

(95 citation statements)

References 20 publications

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“…Previous reports indicated that the computational analysis of H&E images assists pathological diagnosis. 12,13 We also showed that wndchrm recapitulated pathological decisions, since the algorithm achieved acceptable classification | 2231 YASUDA et Al performance using H&E images of gastric cancers with distinct histological grades (Figure 1).…”

Section: Discussionmentioning

confidence: 75%

“…Characteristics of cancerous tissues derived from patients provides diagnostic information regarding the tumors, and allows prediction of therapeutic responses and prognosis . Pathological assessment of cancer specimens stained with hematoxylin and eosin (H&E) is primarily interpreted not only by tissue architecture but also by nuclear morphology of the tumor cells, which has been used for routine clinical diagnosis and computer‐aided pathological diagnosis . Recently, this field has significantly progressed to decipher clinical and biological relevance from such pathological images by combining molecular information such as genomic data …”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9] Pathological assessment of cancer specimens stained with hematoxylin and eosin (H&E) is primarily interpreted not only by tissue architecture but also by nuclear morphology of the tumor cells, which has been used for routine clinical diagnosis 4,10 and computer-aided pathological diagnosis. [11][12][13] Recently, this field has significantly progressed to decipher clinical and biological relevance from such pathological images by combining molecular information such as genomic data. 14,15 Gastric cancer is one of the most common human cancers, and is the second leading cause of cancer-related deaths worldwide.…”

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confidence: 99%

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Computational analysis of morphological and molecular features in gastric cancer tissues

et al. 2020

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Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)‐stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E‐stained images by expression levels of cancer‐associated nuclear ATF7IP/MCAF1 and membranous PD‐L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer‐associated proteins, suggesting the usefulness of biomolecular‐based morphological classification.

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Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Computational analysis of morphological and molecular features in gastric cancer tissues

et al. 2020

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“…Machine learning and deep learning methods have also been used for quality assessment in whole slide images. Senaras et al [38] applied deep learning methods to detect out-of-focus regions in whole slide tissue images that can be avoided in segmentation and classification operations. Wen et al [39,40] utilized machine learning classifiers (SVM, random forest, and CNN) that operate on texture and intensity features extracted from image patches to evaluate the quality of nuclear segmentation results.…”

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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“…In addition to these variations, deep learning algorithms can also be sensitive to image artifacts. Some research has attempted to account for these issues by detecting and pre-screening image artifacts, either by automatically [37][38][39] or manually removing slides with artifacts 19,25,31 . The recent work of Campanella et al made substantial progress towards validating a deep learning system on data completely free of curation 25 .…”

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confidence: 99%

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload

Ianni¹,

Soans²,

Sankarapandian³

et al. 2020

Sci Rep

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Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.Every year in the United States, 12 million skin lesions are biopsied 1 , with over 5 million new skin cancer cases diagnosed 2 . After a skin lesion is biopsied, the tissue is fixed, embedded, sectioned, and stained with hematoxylin and eosin (H&E) on glass slides, ultimately to be examined under microscope by a dermatologist, general pathologist or dermatopathologist who provides a diagnosis for each tissue specimen. Owing to the large variety of over 500 distinct skin pathologies 3 and the severe consequences of a critical misdiagnosis 4 , diagnosis in dermatopathology demands specialized training and education. Although the inter-observer concordance rate in dermatopathology is estimated to be between 90 and 95% 5,6 , there are some distinctions which present frequent disagreement among pathologists, such as in the case of melanoma vs. melanocytic nevi 7-11 . Any system which could improve diagnostic accuracy provides obvious benefits for dermatopathology labs and patients; however, there are substantial benefits also to improving the distribution of pathologists' workloads 12-14 . This can reduce diagnostic turnaround times in several scenarios. For example, when skin biopsies are interpreted initially by a dermatologist or a general pathologist, prior to referral to a dermatopathologist, it can result in a delay of days, sometimes in critical cases. In another common scenario, additional staining is required to identify characteristics of the tissue not captured by standard H&E staining. If those additional stains are not ordered early enough, there can be further delays to diagnosis. An intelligent system to distribute pathology workloads could alleviate some of these bottlenecks in lab workflows. The rise in adoption of digital pathology 1,15 provides an opportunity for the use of deep learning-based methods for closing these gaps in diagnostic reliability and efficiency 16,17 .

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DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning

Cited by 97 publications

References 20 publications

Computational analysis of morphological and molecular features in gastric cancer tissues

Computational analysis of morphological and molecular features in gastric cancer tissues

The Emergence of Pathomics

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload

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