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

Wahab, Noor ul; Miligy, Islam M.; Dodd, Katherine; Sahota, Harvir; Toss, Michael S.; Lu, Wenqi; Jahanifar, Mostafa; Bilal, Mohsin; Graham, Simon; Park, Young; Hadjigeorghiou, Giorgos; Bhalerao, Abhir; Lashen, Ayat; Ibrahim, Asmaa; Katayama, Ayaka; Ebili, Henry O; Parkin, Matthew; Sorell, Tom; Raza, Shan E Ahmed; Hero, Emily; Eldaly, Hesham; Tsang, Yee-Wah; Gopalakrishnan, Kishore; Snead, David; Rakha, Emad A.; Rajpoot, Nasir M.; Minhas, Fayyaz

doi:10.1002/cjp2.256

Cited by 25 publications

(15 citation statements)

References 47 publications

(31 reference statements)

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“…In the specific case, the experts involved are the biologists who select and scan the slides, the pathologists who make the annotations of the slides and the computer scientists who guide and support the annotation process ( 18 ). Indeed, the interaction between the various experts brings out critical issues to be addressed with motivated decisions to make the image acquisition and annotation process feasible and at the same time useful for possible experimentation ( 19 ).…”

Section: Methodsmentioning

confidence: 99%

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

et al. 2022

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Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/

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Section: Methodsmentioning

confidence: 99%

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

et al. 2022

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“…To handle very high-resolution images, a plugin for girder was created, based on OpenSlide. DSA's ease of use and excellent annotation system has led many researchers to choose it as their primary annotation system 36 , 37 or to create new datasets. 38 …”

Section: Software Platforms Specifically Designed For Digital Pathologymentioning

confidence: 99%

Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists

Guerrero

Carvalho

Bocklitz

et al. 2022

Journal of Pathology Informatics

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“…Therefore the evaluation of machine learning algorithms for digital pathology can be very delicate [180]. In [181], Wahab et al have studied, for the classification of different breast cells in H&E WSIs, the quality of annotations in terms of completeness, exhaustiveness, diversity, and agreement. They concluded that standardization of annotation protocols is necessary and proposed a new one.…”

Section: Labeled Data Quality and Quantity: How To Learn The Unexpectedmentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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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.

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

Cited by 25 publications

References 47 publications

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

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