An unsupervised style normalization method for cytopathology images

Chen, Xihao; Yu, Jiaqi; Cheng, Shenghua; Geng, Xiebo; Liu, Sibo; Han, Wei; Hu, Junbo; Chen, Li; Liu, Xiuli; Zeng, Shaoqun

doi:10.1016/j.csbj.2021.06.025

Cited by 12 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, DL-based stain normalization methods using generative adversarial networks (GANs) are a better substitute because the whole dataset of the target style is leveraged as the template to execute color normalization by image-to-image translation. For example, Chen et al [154] proposed a two-stage domain adversarial style normalization framework for cervical cytopathological images and Kang et al [155] presented StainNet by using StainGAN [156] and distillation learning to complete the stain normalization of cervical cell images. Image Super-Resolution.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Third, a Deep Convolutional Gaussian Mixture Model (DCGMM) that jointly optimizes the combined CNN and GMM models. The GAN approach was used for CN cytological imaging by Chen et al [111] and includes an intermediate style removal step.…”

Section: B Color Normalizationmentioning

confidence: 99%

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

et al. 2022

View full text Add to dashboard Cite

Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color processing techniques are usually the main concern, while other areas are frequently ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.

show abstract

“…This enables many-to-one domain stain transfer and the feature preserving loss of [116] is replaced by a structural cycle consistency loss. Chen et al [120] proposed to normalize an input image by style removal and reconstruction, as in [115]. Style removal generates a grayscale image using a color-encoding mask.…”

Section: ) Color Normalizationmentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

View full text Add to dashboard Cite

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

show abstract

An unsupervised style normalization method for cytopathology images

Abstract: Graphical abstract

Cited by 12 publications

References 39 publications

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

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

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Contact Info

Product

Resources

About