Deep learning enables automated scoring of liver fibrosis stages

Yu, Hanry; Wang, Jiahao; Ng, Chan Way; Ma, Yukun; Mo, Shupei; Fong, Eliza Li Shan; Xing, Jiangwa; Song, Ziwei; Xie, Yi; Si, Ke; Wee, Aileen; Welsch, Roy E.; So, Peter T. C.

doi:10.1038/s41598-018-34300-2

Cited by 82 publications

(50 citation statements)

References 29 publications

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“…Many researchers have been inspired by the above, including Vicas et al [60] for fully automating the fibrosis detection procedure with classical computer vision techniques (image processing, conventional machine learning) and convolutional neural networks (CNNs). A CNN model was also employed in Yu et al [61] for the identification of fibrotic areas in liver biopsy specimens. At a later stage, the team made a comparison of the deep model's performance with other conventional classification algorithms.…”

Section: Intelligent Diagnostic Systems For Automated Cpa Detectionmentioning

confidence: 99%

“…In later studies , 121.8 ± 102.5 (mean ± std) patients with viral hepatitis were recruited, with the total number of patients being 122.1 ± 100.2 (mean ± std). Moreover, seven studies depended on patients with liver transplantation [21,[27][28][29][30][31]40], while in more recent studies [45][46][47]51,52,61], non-human tissues were included.…”

Section: Histological Samplesmentioning

confidence: 99%

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Quantification of Liver Fibrosis—A Comparative Study

et al. 2020

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Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic.

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Section: Intelligent Diagnostic Systems For Automated Cpa Detectionmentioning

confidence: 99%

Section: Histological Samplesmentioning

confidence: 99%

Quantification of Liver Fibrosis—A Comparative Study

et al. 2020

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“…Based on these tasks, more abstract functions like disease grading, prognosis prediction, and imaging biomarkers for genetic subtype identification have been established. 4,5 Successful examples include utilization in different types of cancer detection/classification/grading, 6,7 classification of liver cirrhosis, 8 heart failure detection, 9 and classification of Alzheimer plaques. 10 The most commonly used deep learning architectures are convolutional neural networks (CNNs; Figure 1C).…”

Section: Introductionmentioning

confidence: 99%

Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations

et al. 2020

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Objective The microscopic review of hematoxylin‐eosin–stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web‐based deep learning application as proof of concept of how deep learning could enter the pathologic routine. Methods A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient‐weighted class activation maps (Guided Grad‐CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities. Results Our best‐performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad‐CAMs. These patterns were assembled into a classification score to augment decision‐making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance. Significance Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning–aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult‐to‐classify brain lesions.

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“…Manual grading and scoring of liver biopsies can be prone to errors of subjectivity, inexperience and bias. Recent computational methods have sought to reduce this burden and automate the classification process, to aid in diagnosis [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology

Levy

Salas

Christensen

et al. 2019

Preprint

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The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.

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Deep learning enables automated scoring of liver fibrosis stages

Cited by 82 publications

References 29 publications

Quantification of Liver Fibrosis—A Comparative Study

Quantification of Liver Fibrosis—A Comparative Study

Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations

PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology

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