Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

Jose, Laya; Liu, Sidong; Russo, Carlo; Nadort, Annemarie; Ieva, Antonio Di

doi:10.4103/jpi.jpi_103_20

Cited by 48 publications

(26 citation statements)

References 73 publications

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“…This remains for future work. H&E image color variability observed across institutions and labs (Nam et al 2020;Otálora et al 2019;Jose et al 2021) may be caused by differences between stain batches and manufacturers, staining and fixation protocols, and tissue thickness (Veta et al 2014;Bancroft 2008, chap. 10), as well as differences across imaging parameters, including scanner models (Leo et al 2016) and image magnification (Komura and Ishikawa 2018).…”

Section: Discussionmentioning

confidence: 99%

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

White

Woo

Koc

et al. 2022

Preprint

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Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

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Section: Discussionmentioning

confidence: 99%

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

White

Woo

Koc

et al. 2022

Preprint

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“…A larger sample size with more severe and converter cases in the datasets would help train more accurate and robust models as well as produce reliable performance estimates. Other techniques, such as synthetic minority oversampling [29], spherical coordinates transformation [49], and generative adversarial network [50], will be investigated in further study.…”

Section: Discussionmentioning

confidence: 99%

Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT (Preprint)

Feng¹,

Liu²,

Cheng³

et al. 2021

Preprint

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BACKGROUND Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. OBJECTIVE This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. METHODS A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. RESULTS Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. CONCLUSIONS The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. CLINICALTRIAL we performed a retrospective in China. This multicentre study was approved by the institutional review board of the principal investigator’s hospital. Informed consent from patients was exempted due to the retrospective nature of this study.

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“…The model remained effective over a range of PD‐L1 cutoff thresholds [area under the receiver operating characteristics curve (AUC) = 0.67–0.81, p ≤ 0.01], even when different proportions of the labels were randomly shuffled to simulate inter‐pathologist disagreement (AUC = 0.63–0.77, p ≤ 0.03). Generative adversarial networks (GAN) (Table 1) have been explored as an approach for virtual H&E staining [104,105]. Bayramoglu et al [106] used a conditional GAN model for virtual H&E staining of unstained lung tissue, which can allow automating DP workflow.…”

Section: Ai Applications In Lung Pathologymentioning

confidence: 99%

The state of the art for artificial intelligence in lung digital pathology

Viswanathan

Toro

Corredor

et al. 2022

The Journal of Pathology

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Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

Cited by 48 publications

References 73 publications

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT (Preprint)

The state of the art for artificial intelligence in lung digital pathology

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