4.6 Review

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Transfer learning for histopathology images: an empirical study

Tayyab Aitazaz et al.

Summary: This study evaluates the performance of different pre-trained deep learning models for the classification of histopathology images. The results suggest that Vision Transformers are more suitable for histopathology image classification using transfer learning compared to CNN models.

NEURAL COMPUTING & APPLICATIONS (2023)

Article Optics

Learning to autofocus in whole slide imaging via physics-guided deep cascade networks

Qiang Li et al.

Summary: This paper proposes an effective learning-based method for autofocusing in WSI, which achieves accurate and high-speed autofocus without any optical hardware modifications. Experimental results demonstrate the superior autofocusing performance of our method compared to other related methods.

OPTICS EXPRESS (2022)

Article Computer Science, Artificial Intelligence

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Chetan L. Srinidhi et al.

Summary: In this work, we propose novel strategies to leverage both task-agnostic and task-specific unlabeled data for improving representation learning in computational histopathology. Experimental results show that our method outperforms state-of-the-art self-supervised and supervised baselines under limited labeled data, demonstrating the effectiveness of bootstrapping self-supervised pretrained features for task-specific semi-supervised learning.

MEDICAL IMAGE ANALYSIS (2022)

Article Pathology

Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice

Nehal M. Atallah et al.

Summary: This study examined the impact of missing tissue on breast whole slide images (WSI) and found a negative linear correlation between the frequency of missing tissue and scanning time/image file size. Quality control measures improved image quality and reduced WSI failure rates, with missing tissue having little diagnostic consequence.

MODERN PATHOLOGY (2022)

Review Pathology

Digital pathology and artificial intelligence in translational medicine and clinical practice

Vipul Baxi et al.

Summary: Recent technological advancements have enabled the development of digital pathology and AI-based solutions for quantitative pathologic assessments, revolutionizing disease diagnosis and drug development. These innovations provide valuable opportunities in immuno-oncology for deciphering complex pathophysiology and discovering novel biomarkers, while also supporting practitioners in selecting the most appropriate treatment based on patient profiles. The integration of AI-powered analysis tools enhances the traditional role of pathologists in delivering accurate diagnoses and assessing biomarkers, with potential applications in translational medicine and clinical settings.

MODERN PATHOLOGY (2022)

Article Chemistry, Analytical

Stain Style Transfer for Histological Images Using S3CGAN

Jiann-Shu Lee et al.

Summary: This study proposes a new CycleGAN-based stain transfer model, called S3CGAN, equipped with a specialized color classifier structure. The specialized color classifier assists the generative network in overcoming the instability caused by insufficient representativeness of training data. The pretrained color classifier provides correct color information feedback to the generator, enabling it to generate superior results. The model uses U-Net and a Markovian discriminator to enhance structural retention ability, resulting in high-fidelity image generation.

SENSORS (2022)

Article Computer Science, Theory & Methods

Cytology Image Analysis Techniques Toward Automation: Systematically Revisited

Shyamali Mitra et al.

Summary: Cytology is a branch of pathology that focuses on diagnosing cancer and inflammatory conditions through microscopic examination of cells. Automation in cytology, specifically through image analysis techniques, has advanced significantly in various types of cytology.

ACM COMPUTING SURVEYS (2022)

Article Medicine, General & Internal

Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities

Diana Montezuma et al.

Summary: This paper reports the experience of a high-volume private laboratory in transitioning to digital pathology, addressing the main challenges in implementing DP in a private practice setting. They use two high-capacity scanners to digitize histology slides and continuously improve WSI quality through optimization and collaboration.

DIAGNOSTICS (2022)

Article Pathology

Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations

Noorul Wahab et al.

Summary: This paper addresses the importance of annotations in Computational Pathology (CPath) projects and the current lack of well-defined guidelines. By presenting a large-scale annotation exercise, the authors provide annotation guidelines and best practice recommendations for CPath projects.

JOURNAL OF PATHOLOGY CLINICAL RESEARCH (2022)

Review Pathology

The ethical challenges of artificial intelligence-driven digital pathology

Francis McKay et al.

Summary: Digital pathology has opened up new possibilities for healthcare, especially in artificial intelligence-driven research. However, there is little scholarly debate on the ethics of digital pathology for AI research. This paper summarises four key ethical issues to consider when deploying AI infrastructures in pathology, namely privacy, choice, equity, and trust. The importance of robust public governance mechanisms in AI-driven digital pathology is emphasized.

JOURNAL OF PATHOLOGY CLINICAL RESEARCH (2022)

Article Computer Science, Information Systems

The Xception model: A potential feature extractor in breast cancer histology images classification

Shallu Sharma et al.

Summary: Computer-assisted pathology analysis is a crucial field in health informatics. This study demonstrates the effectiveness of the pre-trained Xception model for breast cancer histopathological image classification, surpassing handcrafted approaches. The evaluation metrics show superior performance compared to existing techniques.

ICT EXPRESS (2022)

Article Computer Science, Artificial Intelligence

Weakly Supervised Object Detection Using Proposal- and Semantic-Level Relationships

Dingwen Zhang et al.

Summary: Weakly supervised object detection has received great attention in recent years in the computer vision community. However, existing approaches mostly focus on visual appearance and ignore the use of context information. This paper proposes a weakly supervised learning framework that incorporates proposal-level and semantic-level context, leading to improved learning performance through deep multiple instance reasoning. Experimental results demonstrate the superior performance of the proposed approach on widely used benchmarks.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Article Oncology

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images

Jeffrey Boschman et al.

Summary: This study compared eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, finding that while color normalization does not consistently improve classification on single-center data, it significantly enhances accuracy on multi-center datasets. Introducing a novel augmentation strategy by mixing color-normalized images using three algorithms consistently improves diagnosis of test images from external centers, showing the potential for reliable color normalization in AI-based digital pathology diagnosis of cancers from multiple centers.

JOURNAL OF PATHOLOGY (2022)

Article Pathology

Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Wei Ba et al.

Summary: This study evaluated the use of deep learning (DL) assistance in the diagnosis of gastric cancer by pathologists. The results demonstrated that pathologists with DL assistance achieved higher sensitivity in detecting gastric cancer and had shorter review time per slide, indicating improved accuracy and efficiency.

MODERN PATHOLOGY (2022)

Article Computer Science, Information Systems

Staining condition visualization in digital histopathological whole-slide images

Yiping Jiao et al.

Summary: This study proposes an intuitive method to visualize the color style of Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs) and validates its effectiveness in lung cancer research.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Multidisciplinary Sciences

Automated quality assessment of large digitised histology cohorts by artificial intelligence

Maryam Haghighat et al.

Summary: Research using whole slide images of histopathology slides has been on the rise in recent years. In this study, a deep neural network was trained to automate the quality control process of a large retrospective cohort of prostate cases, maximizing the utility of these images for research purposes.

SCIENTIFIC REPORTS (2022)

Review Oncology

Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape

Muhammad Joan Ailia et al.

Summary: The integration of digital pathology with artificial intelligence enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. In the past five years, there has been an increasing trend in patent filings, primarily focusing on the digitization of pathological images and AI technologies that support the vital role of pathologists.

CANCERS (2022)

Article Medicine, General & Internal

The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters

Kai Rakovic et al.

Summary: The deployment of digital pathology and artificial intelligence in the diagnosis of prostate cancer has attracted particular interest. A survey conducted among Prostate Cancer UK supporters revealed that the majority of respondents were supportive of these technologies, seeing their potential in improving workflow efficiency and facilitating clinical discussions. However, concerns were raised regarding data security and reliability.

DIAGNOSTICS (2022)

Review Surgery

Progress on deep learning in digital pathology of breast cancer: a narrative review

Jingjin Zhu et al.

Summary: This article summarizes the application of DL in breast cancer pathology through literature review and discusses its advantages and challenges in clinical practice. DL has achieved significant progress in breast cancer pathology diagnosis, but further research is needed to realize digitization and automation.

GLAND SURGERY (2022)

Article Computer Science, Artificial Intelligence

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer

Yoni Schirris et al.

Summary: This study proposes a deep learning-based weak label learning method for analyzing tumor tissue whole slide images without pixel-level or tile-level annotations. They utilize self-supervised pre-training and heterogeneity-aware deep multiple instance learning. The method is applied to the prediction of homologous recombination deficiency and microsatellite instability, achieving improved performance.

MEDICAL IMAGE ANALYSIS (2022)

Article Computer Science, Hardware & Architecture

RestainNet: A self-supervised digital re-stainer for stain normalization

Bingchao Zhao et al.

Summary: This paper proposes a new approach for stain normalization in pathological images called RestainNet, which achieves outstanding performance in terms of color correctness and structure preservation. The proposed RestainNet also outperforms state-of-the-art methods in segmentation and classification tasks.

COMPUTERS & ELECTRICAL ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

Cervical cytopathology image refocusing via multi-scale attention features and domain normalization

Xiebo Geng et al.

Summary: In this paper, the authors propose a refocusing method for cervical cytopathology images using multi-scale attention features and domain normalization. Their method consists of a domain normalization network (DNN) and a refocusing network (RFN). The DNN is used to normalize unseen unsupervised domains into a seen supervised domain, while the RFN is designed to enhance the reconstruction of cell nucleus and cytoplasm. The authors demonstrate the superiority of their method through extensive experiments and show that the refocused images can improve the performance of subsequent high-level analysis tasks. They also release the refocusing dataset and source codes to promote further research in this field.

MEDICAL IMAGE ANALYSIS (2022)

Article Multidisciplinary Sciences

Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network

Zabit Hameed et al.

Summary: This paper proposes a deep learning approach for automatic classification of breast cancer microscopy images, achieving good results. The study found that the performance was better on the original dataset, and stain normalization techniques could not surpass the results of the original dataset.

SCIENTIFIC REPORTS (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Richard J. Chen et al.

Summary: A new ViT architecture called HIPT is introduced to leverage the hierarchical structure inherent in gigapixel WSIs, with impressive performance on 9 slide-level tasks.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

Proceedings Paper Acoustics

HISTOKT: CROSS KNOWLEDGE TRANSFER IN COMPUTATIONAL PATHOLOGY

Ryan Zhang et al.

Summary: In this paper, the lack of well-annotated datasets in computational pathology is addressed as a barrier to the application of deep learning techniques in classifying medical images. The study takes a data-centric approach to the transfer learning problem in order to examine the potential existence of generalizable knowledge between histopathological datasets. The results show that pretraining is most beneficial for hard to learn, multi-class datasets and a two-stage learning framework with a large source domain allows for better utilization of smaller datasets. The use of weight distillation also enables models trained on purely histopathological features to outperform models using external natural image data.

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (2022)

Proceedings Paper Computer Science, Artificial Intelligence

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Hongrun Zhang et al.

Summary: This paper investigates the multiple instance learning problem in the classification of histopathology whole slide images, and proposes a double-tier MIL framework for small sample cohorts. It also introduces the concept of pseudo-bags and utilizes attention-based MIL framework to calculate instance probability. The proposed method outperforms other approaches on multiple datasets.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology

Yunlong Zhang et al.

Summary: This paper establishes a benchmark to evaluate the performance of deep neural networks on corrupted pathology images. The results show that various deep neural network models suffer from decreased accuracy and unreliable confidence estimation on corrupted images.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT II (2022)

Proceedings Paper Engineering, Biomedical

TOWARDS MEASURING DOMAIN SHIFT IN HISTOPATHOLOGICAL STAIN TRANSLATION IN AN UNSUPERVISED MANNER

Zeeshan Nisar et al.

Summary: This article investigates domain shift in digital histopathology and proposes the use of PixelCNN and domain shift metric to detect and quantify domain shift. The study reveals a strong correlation between these methods and generalization performance, providing a mechanism to estimate model performance on unseen target data.

2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022) (2022)

Review Computer Science, Information Systems

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

Neel Kanwal et al.

Summary: This paper presents 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.

IEEE ACCESS (2022)

Article Pathology

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Wouter Bulten et al.

Summary: The Gleason score is crucial for prostate cancer prognosis, and AI systems based on deep learning can help improve pathologist performance in grading. However, the performance of AI systems may degrade in the presence of artifacts. Integrating pathologists' expertise with AI feedback can lead to better results.

MODERN PATHOLOGY (2021)

Article Computer Science, Interdisciplinary Applications

Style transfer strategy for developing a generalizable deep learning application in digital pathology

Seo Jeong Shin et al.

Summary: This study explored the effect of image-to-image style transfer on diagnostic performance using ovarian cancer pathology images, showing improved performance of a deep learning model after style transfer. This successful application of style transfer technology can help generalize deep learning models to small image sets in the field of digital pathology.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Pathology

Quick Annotator: an open-source digital pathology based rapid image annotation tool

Runtian Miao et al.

Summary: Image-based biomarker discovery often requires accurate segmentation of histologic structures in digital pathology whole slide images (WSIs). An open-source tool called Quick Annotator (QA) is presented here to improve annotation efficiency significantly through the use of deep learning. Efficiency gains were demonstrated in three use cases, showing the potential value of QA in fully annotating WSIs for downstream biomarker studies.

JOURNAL OF PATHOLOGY CLINICAL RESEARCH (2021)

Article Computer Science, Artificial Intelligence

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

Lyndon Chan et al.

Summary: Methods for weakly-supervised semantic segmentation excel on datasets they were developed on, but struggle on other datasets due to varying characteristics and challenges presented by different domains. More work is needed for a generalizable approach to weakly-supervised semantic segmentation.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

Review Oncology

Deep computational pathology in breast cancer

Andrea Duggento et al.

Summary: Deep Learning algorithms utilize large and complex datasets for cross-domain prediction and classification, particularly excelling in computer vision tasks. In medical imaging, DL strategies can outperform human experts significantly in the analysis of histopathology images. The shift towards semi-supervised learning methods provides more flexibility and applicability in the development of specialized DL algorithms for pathology.

SEMINARS IN CANCER BIOLOGY (2021)

Review Biology

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Massimo Salvi et al.

Summary: Deep learning frameworks have become the main methodology for analyzing medical images, especially in digital pathology. These frameworks can handle various image analysis tasks, such as classification, detection, and segmentation. Recent studies have shown that integrating traditional image processing methods for pre- and post-processing within a deep learning pipeline can improve model performance significantly.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Oncology

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Yijiang Chen et al.

Summary: This study evaluated a computer-aided QC pipeline for enhancing reproducibility in the QC process of WSI datasets, showing improvements in curation consistency and detection of batch effects.

JOURNAL OF PATHOLOGY (2021)

Review Medicine, Research & Experimental

Artificial intelligence and computational pathology

Miao Cui et al.

Summary: Computational pathology, driven by data processing and clinical informatics, offers innovative solutions for patient care. However, challenges such as data integration, hardware limitations, and talent development need to be addressed for the field to advance further.

LABORATORY INVESTIGATION (2021)

Article Computer Science, Artificial Intelligence

Deep neural network models for computational histopathology: A survey

Chetan L. Srinidhi et al.

Summary: This paper presents a comprehensive review of state-of-the-art deep learning approaches used in histopathological image analysis. Through a survey of over 130 papers, the progress in the field based on different machine learning strategies is reviewed. Additionally, the paper discusses the application of deep learning in survival models and highlights the challenges and limitations of current deep learning methods, as well as potential directions for future research.

MEDICAL IMAGE ANALYSIS (2021)

Article Engineering, Electrical & Electronic

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang et al.

Summary: Transfer learning aims to improve the performance of target learners by transferring knowledge from related source domains, reducing the reliance on target-domain data. This survey aims to systematize and summarize existing research studies in order to help readers understand the current status and ideas in the area of transfer learning.

PROCEEDINGS OF THE IEEE (2021)

Article Chemistry, Multidisciplinary

Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers

Hyun-Jong Jang et al.

Summary: The study found that DL classifiers trained on the same tissue preparations and cancer types can achieve high diagnostic accuracy, but their performance drops significantly when applied to different tissue modalities and cancer types.

APPLIED SCIENCES-BASEL (2021)

Article Urology & Nephrology

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies

Felicia Marginean et al.

Summary: The study aimed to develop an artificial intelligence algorithm for improved standardisation in Gleason grading in prostate cancer biopsies, using machine learning and convolutional neural networks. The algorithm showed high accuracy in detecting cancer areas and assigning Gleason patterns correctly, achieving similar results as pathologists with low intraobserver variability.

EUROPEAN UROLOGY FOCUS (2021)

Article Computer Science, Artificial Intelligence

Towards histopathological stain invariance by Unsupervised Domain Augmentation using generative adversarial networks

Jelica Vasiljevic et al.

Summary: This paper introduces an unsupervised augmentation approach based on adversarial image-to-image translation, which helps to train stain invariant convolutional neural networks. Significant improvements are demonstrated by training the network on a commonly used staining modality and applying it to differently stained images.

NEUROCOMPUTING (2021)

Article Multidisciplinary Sciences

EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control

Christian Marzahl et al.

Summary: EXACT is an open-source online platform that facilitates collaborative interdisciplinary analysis of image data, supporting diverse applications such as medical imaging, deep learning, and sound spectroscopy. It allows for effective annotation tasks across multiple domains and can adapt to various research areas with its flexible plugin system.

SCIENTIFIC REPORTS (2021)

Article Computer Science, Information Systems

Stain Standardization Capsule for Application-Driven Histopathological Image Normalization

Yushan Zheng et al.

Summary: Color consistency is crucial for developing robust deep learning methods for histopathological image analysis. A novel color standardization module has been proposed in this paper to generate uniform stain separation outputs for histopathological images. The experimental results have demonstrated that the proposed module is effective in improving the performance of histopathological image analysis.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Computer Science, Information Systems

The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis

Alexander Wright et al.

Summary: This study explores the impact of digital slide image quality on automated image analysis, focusing on the algorithm for generating tumor:stroma ratio. It was found that algorithm performance was lowest on poorly differentiated tissue. Removing images with quality issues improved accuracy but reduced the dataset size.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Computer Science, Information Systems

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology

Romain Mormont et al.

Summary: In this study, multi-task learning was explored as a method for pre-training models for classification tasks in digital pathology. By assembling and transforming multiple datasets, a pool of 22 classification tasks and nearly 900k images was successfully created. Experimental results showed that our models either significantly outperformed ImageNet pre-trained models or provided comparable performance on different target tasks.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Engineering, Biomedical

Data-efficient and weakly supervised computational pathology on whole-slide images

Ming Y. Lu et al.

Summary: The CLAM method utilizes attention-based learning to identify subregions with high diagnostic value for accurate classification of whole-slide images. It can localize well-known morphological features without the need for spatial labels, outperforming standard weakly supervised classification algorithms, and adapt to independent test cohorts, smartphone microscopy, and varying tissue content.

NATURE BIOMEDICAL ENGINEERING (2021)

Article Multidisciplinary Sciences

Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning

Atsushi Teramoto et al.

Summary: An automated weakly supervised method using attention-based deep multiple instance learning was developed to classify benign and malignant lung cells in cytological images. The method showed a high classification accuracy of 0.916 and was able to automatically classify images without the need for complex annotations.

SCIENTIFIC REPORTS (2021)

Article Medicine, General & Internal

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

Filippo Fraggetta et al.

Summary: The article discusses the implementation of digital pathology workflows and how to promote the adoption of digital pathology workflows in pathology laboratories. Consensus-based recommendations from experts cover every step of the digital workflow, emphasizing the importance of interoperability, automation, and process tracking.

DIAGNOSTICS (2021)

Review Biochemistry & Molecular Biology

Deep learning in histopathology: the path to the clinic

Jeroen van der Laak et al.

Summary: Recent advancements in machine learning have shown great potential to enhance medical diagnostics, particularly in the field of histopathology. However, challenges remain in implementing these techniques in clinical settings.

NATURE MEDICINE (2021)

Article Biochemical Research Methods

OpenPhi: an interface to access Philips iSyntax whole slide images for computational pathology

Nita Mulliqi et al.

Summary: Digital pathology benefits from computational methods like deep learning, but interoperability issues between whole slide image formats from different scanner vendors pose a challenge for algorithm developers. OpenPhi-Open PatHology Interface offers a solution by providing seamless access to iSyntax format, specifically used by Philips Ultra Fast Scanner, the first FDA-approved digital pathology scanner, making it easier for developers to work with vendor-neutral applications.

BIOINFORMATICS (2021)

Article Pathology

Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Marlen Runz et al.

Summary: This paper investigates the potential of CycleGAN for color normalization in histological images, using daily clinical data and considering variability in internal staining protocols. Through training the generator and discriminator networks, image normalization is achieved and validated on various datasets.

DIAGNOSTIC PATHOLOGY (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Deep Ordinal Focus Assessment for Whole Slide Images

Tome Albuquerque et al.

Summary: Medical image quality assessment is important in image acquisition processes, and a new deep ordinal learning approach was introduced for focus assessment in whole slide images. Using ordinal losses instead of nominal cross-entropy loss, the proposed model outperformed other methods in the literature, achieving an accuracy of 94.4% in the FocusPath database.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification

Jerry Wei et al.

Summary: Curriculum learning can be effectively applied to histopathology image classification, where the range of difficulty among examples is inherent and annotator agreement can serve as a proxy for difficulty. In this paper, a simple curriculum learning method is proposed and demonstrated to improve colorectal polyp classification accuracy by 4.5% compared to traditional training methods. This work encourages researchers to consider more creative and rigorous approaches when applying curriculum learning to challenging tasks.

2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 (2021)

Article Biochemistry & Molecular Biology

An unsupervised style normalization method for cytopathology images

Xihao Chen et al.

Summary: This article proposes an unsupervised method to normalize cytopathology image styles through a two-stage style normalization framework, achieving superior results on six cervical cell datasets from different hospitals and scanners. The method greatly improves the recognition accuracy of lesion cells on unseen cytopathology images, meaningful for model generalization.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

Review Medicine, General & Internal

Diagnostic Pitfalls of Digital Microscopy Versus Light Microscopy in Gastrointestinal Pathology: A Systematic Review

Wangpan Shi et al.

Summary: Digital microscopy is a cutting-edge technology in pathology, with potential benefits in efficiency and diagnosis accuracy, but there are common diagnostic pitfalls in gastrointestinal pathology that need to be addressed. Studies have shown discrepancies between digital microscopy and light microscopy, mainly in difficulties in identification, missed organisms, and technical issues. Suggestions for further gastrointestinal cases signing out by digital microscopy include using systematic 20x scans for initial diagnosis and increasing magnification for challenging cases.

CUREUS JOURNAL OF MEDICAL SCIENCE (2021)

Proceedings Paper Optics

Colorimetrical Evaluation of Color Normalization Methods for H&E-Stained Images

Jocelyn Liu et al.

Summary: Color normalization is an important preprocessing step for deep learning algorithms used in aiding pathology diagnoses with whole-slide images. Existing methods for color-normalizing H&E-stained images have not been quantitatively defined or evaluated beyond visual comparison. The study proposes a quantitative metric called color normality, and experiment results show limitations in widely used methods like Macenko and Vahadane in fully preserving the color gamut volume, resulting in reduced color normality.

MEDICAL IMAGING 2021 - DIGITAL PATHOLOGY (2021)

Review Medicine, Research & Experimental

Developing image analysis pipelines of whole-slide images: Pre- and post-processing

Byron Smith et al.

Summary: Deep learning is pushing the boundaries of digital pathology by going beyond simple digitization and telemedicine, potentially becoming a disruptive technology that can reduce processing time and improve anomaly detection. However, integrating deep learning into standard laboratory workflow requires more steps than just training and testing a model.

JOURNAL OF CLINICAL AND TRANSLATIONAL SCIENCE (2021)

Article Computer Science, Interdisciplinary Applications

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Mahdi S. Hosseini et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

Article Computer Science, Artificial Intelligence

StainCNNs: An efficient stain feature learning method

Gaoyi Lei et al.

NEUROCOMPUTING (2020)

Article Pathology

Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses

Liron Pantanowitz et al.

DIAGNOSTIC PATHOLOGY (2020)

Article Multidisciplinary Sciences

Weakly-supervised learning for lung carcinoma classification using deep learning

Fahdi Kanavati et al.

SCIENTIFIC REPORTS (2020)

Article Computer Science, Artificial Intelligence

GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images

Anubha Gupta et al.

MEDICAL IMAGE ANALYSIS (2020)

Article Multidisciplinary Sciences

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

Zhigang Song et al.

NATURE COMMUNICATIONS (2020)

Review Medicine, General & Internal

Digital Pathology: Advantages, Limitations and Emerging Perspectives

Stephan W. Jahn et al.

JOURNAL OF CLINICAL MEDICINE (2020)

Proceedings Paper Biochemical Research Methods

The effect of blurring on lung cancer subtype classification accuracy of convolutional neural networks

Tejal Nair et al.

2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (2020)

Article Engineering, Electrical & Electronic

Automatic Quality Evaluation of Whole Slide Images for the Practical Use of Whole Slide Imaging Scanner

Hossain Md Shakhawat et al.

ITE TRANSACTIONS ON MEDIA TECHNOLOGY AND APPLICATIONS (2020)

Review Computer Science, Artificial Intelligence

Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

Maximilian E. Tschuchnig et al.

PATTERNS (2020)

Review Pathology

Introduction to digital pathology and computer-aided pathology

Soojeong Nam et al.

JOURNAL OF PATHOLOGY AND TRANSLATIONAL MEDICINE (2020)

Article Computer Science, Information Systems

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Md Mamunur Rahaman et al.

IEEE ACCESS (2020)

Article Chemistry, Applied

Assessing color performance of whole-slide imaging scanners for digital pathology

Wei-Chung Cheng et al.

COLOR RESEARCH AND APPLICATION (2019)

Article Biochemistry & Molecular Biology

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Gabriele Campanella et al.

NATURE MEDICINE (2019)

Article Biotechnology & Applied Microbiology

Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images

Jian Ren et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2019)

Article Computer Science, Artificial Intelligence

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

Mahdi S. Hosseini et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2019)

Article Oncology

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology

Kaustav Bera et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2019)

Article Biotechnology & Applied Microbiology

Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology

Sebastian Otalora et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2019)

Review Computer Science, Artificial Intelligence

Computational normalization of H&E-stained histological images: Progress, challenges and future potential

Thaina A. Azevedo Tosta et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2019)

Article Biotechnology & Applied Microbiology

Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks

Justin Tyler Pontalba et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2019)

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

Mahdi S. Hosseini et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Engineering, Biomedical

INK REMOVAL FROM HISTOPATHOLOGY WHOLE SLIDE IMAGES BY COMBINING CLASSIFICATION, DETECTION AND IMAGE GENERATION MODELS

Sharib Ali et al.

2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) (2019)

Article Medicine, General & Internal

A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology

Andreea Anghel et al.

FRONTIERS IN MEDICINE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Farhad Ghazvinian Zanjani et al.

JOURNAL OF MEDICAL IMAGING (2019)

Article Oncology

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

Andrew Janowczyk et al.

JCO CLINICAL CANCER INFORMATICS (2019)

Review Medicine, General & Internal

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

Yves-Remi Van Eycke et al.

FRONTIERS IN MEDICINE (2019)

Article Biochemical Research Methods

Assessing microscope image focus quality with deep learning

Samuel J. Yang et al.

BMC BIOINFORMATICS (2018)

Article Engineering, Biomedical

Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology

Gabriele Campanella et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2018)

Article Computer Science, Interdisciplinary Applications

Adversarial Stain Transfer for Histopathology Image Analysis

Aicha BenTaieb et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)

Article Computer Science, Artificial Intelligence

Deep visual domain adaptation: A survey

Mei Wang et al.

NEUROCOMPUTING (2018)

Proceedings Paper Optics

Practical image quality evaluation for whole slide imaging scanner

Hossain Md Shakhawat et al.

BIOMEDICAL IMAGING AND SENSING CONFERENCE (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Comparison of deep transfer learning strategies for digital pathology

Romain Mormont et al.

PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2018)

Review Cell Biology

Colour in digital pathology: a review

Emily L. Clarke et al.

HISTOPATHOLOGY (2017)

Article Multidisciplinary Sciences

QuPath: Open source software for digital pathology image analysis

Peter Bankhead et al.

SCIENTIFIC REPORTS (2017)

Review Medical Laboratory Technology

The Diagnostic Concordance of Whole Slide Imaging and Light Microscopy A Systematic Review

Edward Goacher et al.

ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

A Quantitative Assessment of Image Normalization for Classifying Histopathological Tissue of the Kidney

Michael Gadermayr et al.

PATTERN RECOGNITION (GCPR 2017) (2017)

Article Medical Laboratory Technology

Computational Pathology A Path Ahead

David N. Louis et al.

ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2016)

Article Computer Science, Interdisciplinary Applications

Stain Specific Standardization of Whole-Slide Histopathological Images

Babak Ehteshami Bejnordi et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Computer Science, Interdisciplinary Applications

Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

Abhishek Vahadane et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images

Patrick Leo et al.

JOURNAL OF MEDICAL IMAGING (2016)

Article Engineering, Biomedical

A Complete Color Normalization Approach to Histopathology Images Using Color Cues Computed From Saturation-Weighted Statistics

Xingyu Li et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2015)

Article

Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

Katarzyna Tomczak et al.

Wspolczesna Onkologia-Contemporary Oncology (2015)

Article Medicine, Research & Experimental

Objective and Subjective Assessment of Digital Pathology Image Quality

Prarthana Shrestha et al.

AIMS MEDICAL SCIENCE (2015)

Article Engineering, Electrical & Electronic

A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation

Khosro Bahrami et al.

IEEE SIGNAL PROCESSING LETTERS (2014)

Article Engineering, Biomedical

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Adnan Mujahid Khan et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2014)

Article Radiology, Nuclear Medicine & Medical Imaging

Color accuracy and reproducibility in whole slide imaging scanners

Prarthana Shrestha et al.

JOURNAL OF MEDICAL IMAGING (2014)

Article Medical Laboratory Technology

Cytological artifacts masquerading interpretation

Khushboo Sahay et al.

JOURNAL OF CYTOLOGY (2013)

Review Biochemical Research Methods

Microfluidic sample preparation for diagnostic cytopathology

Albert J. Mach et al.

LAB ON A CHIP (2013)

Article Biochemical Research Methods

Icy: an open bioimage informatics platform for extended reproducible research

Fabrice de Chaumont et al.

NATURE METHODS (2012)

Article Multidisciplinary Sciences

SurfaceSlide: A Multitouch Digital Pathology Platform

Yinhai Wang et al.

PLOS ONE (2012)

Article Oncology

Balancing image quality and compression factor for special stains whole slide images

Anurag Sharma et al.

ANALYTICAL CELLULAR PATHOLOGY (2012)

Article Pathology

Lossless Compression of JPEG2000 Whole Slide Images Is Not Required for Diagnostic Virtual Microscopy

Thomas Kalinski et al.

AMERICAN JOURNAL OF CLINICAL PATHOLOGY (2011)

Article Pathology

Digital Imaging in Pathology: Whole-Slide Imaging and Beyond

Farzad Ghaznavi et al.

Annual Review of Pathology-Mechanisms of Disease (2011)

Editorial Material Engineering, Biomedical

Special issue on whole slide microscopic image processing

Olivier Lezoray et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2011)

Review Engineering, Biomedical

Computational pathology: Challenges and promises for tissue analysis

Thomas J. Fuchs et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2011)

Article Pathology

Quality evaluation of virtual slides using methods based on comparing common image areas

Slawomir Walkowski et al.

DIAGNOSTIC PATHOLOGY (2011)

Article Pathology

Color standardization and optimization in Whole Slide Imaging

Yukako Yagi

DIAGNOSTIC PATHOLOGY (2011)

Article Anatomy & Morphology

Autofocusing in computer microscopy: Selecting the optimal focus algorithm

Y Sun et al.

MICROSCOPY RESEARCH AND TECHNIQUE (2004)

Article Computer Science, Software Engineering

Color transfer between images

E Reinhard et al.

IEEE COMPUTER GRAPHICS AND APPLICATIONS (2001)