Digital pathology: semper ad meliora

Es, Simone L. Van

doi:10.1016/j.pathol.2018.10.011

Cited by 38 publications

(30 citation statements)

References 60 publications

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“…Granulocytes are historically difficult to assess on WSI. 1 Chronic rhinosinusitis (CRS) is a heterogenous inflammatory condition. CRS subtype is diagnosed via histopathology.…”

Section: Haemosiderotic Fibrolipomatous Tumour -A Case Reportmentioning

confidence: 99%

Lanthanum deposition in the gastric mucosa

Winden

Khor

2020

Pathology

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“…Granulocytes are historically difficult to assess on WSI. 1 Chronic rhinosinusitis (CRS) is a heterogenous inflammatory condition. CRS subtype is diagnosed via histopathology.…”

Section: Haemosiderotic Fibrolipomatous Tumour -A Case Reportmentioning

confidence: 99%

Lanthanum deposition in the gastric mucosa

Winden

Khor

2020

Pathology

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“…Traditional diagnosis of histological samples is based on manual examination of the morphological features of specimens by skilled pathologists. In recent years, the use of computer-aided technologies for aiding in this task is an emerging trend, with the main goal to reduce the intra and inter-observer variability [1]. Such technologies are intended to improve the diagnosis of pathological slides to be more reproducible, increase objectivity, and save time in the routine examination of samples [2,3].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

Ortega

Halicek

Fabelo

et al. 2020

Sensors

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Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

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“…These regulatory developments have ushered a steady increase in the recognition of the potential of DP to enhance pathologists' productivity and accuracy. The details of such enhancements remain largely speculative, but they include facilitating access to subspecialty expertise, as well as integration of AI-based applications that can improve diagnosis, classification, tumor grading, and stain interpretation [11]. For instance, ConvNet achieved outstanding results in differentiating between benign and malignant skin lesions [12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Achi

Khoury

2020

Cancers

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Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

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Digital pathology: semper ad meliora

Cited by 38 publications

References 60 publications

Lanthanum deposition in the gastric mucosa

Lanthanum deposition in the gastric mucosa

Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

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