Deep learning‐based semantic segmentation of non‐melanocytic skin tumors in whole‐slide histopathological images

Wang, Linyan; Shao, Anwen; Huang, Fengbo; Liu, Zhengyun; Wang, Yaqi; Huang, Xingru; Ye, Juan

doi:10.1111/exd.14782

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Therefore, we believe that these challenges can be solved through automated analysis through deep learning ( Wang et al, 2021 ), which has already shown great promise in medical image analysis, spanning from screening, diagnosing to prognosis prediction in varying disease such as lung cancer, skin cancer, and breast cancer ( Chaudhary et al, 2018 ; Roy-Cardinal et al, 2019 ; Ying et al, 2022 ; Wang et al, 2023 ). Machine learning could detect carotid plaques ( Chen et al, 2021 ), but requires manual segmentation.…”

Section: Introductionmentioning

confidence: 99%

A deep learning algorithm to identify carotid plaques and assess their stability

He,

Yang,

Wang

et al. 2024

Front. Artif. Intell.

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BackgroundCarotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.MethodsA total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People’s Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.ResultsModeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.ConclusionDeep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

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