Deep learning for computational cytology: A survey

Jiang, Hao; Zhou, Yanning; Lin, Yi; Chan, Ronald; Liu, Jiang; Chen, Hao

doi:10.1016/j.media.2022.102691

Cited by 58 publications

(25 citation statements)

References 114 publications

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“…As mentioned in the earlier part of the article, CNN is successfully used in the computational model preparation from WSI 7 . AI, particularly CNN is used in almost every cytology samples 20 . CNN can bypass the manual extraction of the cytology features.…”

Section: Artificial Intelligence In Computational Pathologymentioning

confidence: 99%

An emerging era of computational cytology

Dey

Bansal

Saini

2023

Diagnostic Cytopathology

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Background The significant advancement in digital imaging, data management, advanced computational power, and artificial neural network have an immense impact on the field of cytology. The amalgamation of these areas has generated a newer discipline known as computational cytology. Aims and objective In To discuss the various important aspects of computational cytology. Materials and methods We reviewed the different studies published in English during the last few years on computational cytology. Result Computational cytology is a newer and emerging discipline in pathology that deals with the patient's meta‐data and digital image data to make a mathematical model to produce diagnostic interpretations and predictions. The role of the cytologist is now changing from a simple observational scientist and slide interpreter to a dynamic and integrated multi‐parametric prediction‐based scientist. Conclusion In the current stage, the cytologist must understand the situation and should have a vision of the complete scenario on computational cytology.

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Section: Artificial Intelligence In Computational Pathologymentioning

confidence: 99%

An emerging era of computational cytology

Dey

Bansal

Saini

2023

Diagnostic Cytopathology

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“…Effective Feature Extractor. Feature extractors are used to learn discriminative features of cytology images in computational cytology [159]. The feature representation capability of the feature extractor will greatly affect the downstream tasks (cervical cell identification, abnormal cell detection, and cell region segmentation).…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future researches in automated cervical cytology screening.

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“…Advances in cytopathology vis-à-vis increased automation can bring several benefits to all stakeholders in the healthcare space [18][19][20][21][22] . The adoption of computer assisted Papanicolaou ('Pap') test screening helped laboratories address overwhelming numbers of tests that formerly required manual screening, leading to inevitable workflow backlogs and diagnostic errors resulting from overwork.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Validation Study of an Improved Semi-Autonomous Urine Cytology Assessment Tool: AutoParis-X

Levy

Chan

Marotti

et al. 2023

Preprint

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Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy and reliability of bladder cancer screening, which has heretofore relied on semi-subjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices, e.g., The Paris System for Reporting Urinary Cytology (TPS), algorithms to emulate semi-autonomous diagnostic decision-making have lagged behind, in part due to the complex and nuanced nature of urine cytology reporting. In this study, we report on a deep learning tool, AutoParis-X, which can facilitate rapid semi-autonomous examination of urine cytology specimens. Through a large-scale retrospective validation study, results indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide-variety of cell and cluster-related information across a slide to yield an Atypia Burden Score (ABS) that correlates closely with overall specimen atypia, predictive of TPS diagnostic categories. Importantly, this approach accounts for challenges associated with assessment of overlapping cell cluster borders, which improved the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm (NC) ratio for cells in these clusters. We developed an interactive web application that is publicly available and open-source, which features a simple, easy-to-use display for examining urine cytology whole-slide images (WSI) and determining the atypia level of specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semi-automated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms via head-to-head clinical trials.

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Deep learning for computational cytology: A survey

Cited by 58 publications

References 114 publications

An emerging era of computational cytology

An emerging era of computational cytology

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Large-Scale Validation Study of an Improved Semi-Autonomous Urine Cytology Assessment Tool: AutoParis-X

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