Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning

Jin, Ke; Shen, Yiqing; Lu, Yizhou; Deng, Junwei; Wright, Jason D.; Zhang, Yan; Huang, Qin; Wang, Dadong; Jing, Naifeng; Liang, Xiaoyao; Jiang, Fan

doi:10.1038/s41374-021-00537-1

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…In addition, the number and type of cells are very large compared to cervical cytology, making judgment difficult for instance. A lot of deep-learning research on cervical cytopathology has been undertaken, and the accuracy is very high 15 – 17 . This is because the state of each cell can be judged.…”

Section: Discussionmentioning

confidence: 99%

Effective deep learning for oral exfoliative cytology classification

Sukegawa

Tanaka

Nakano

et al. 2022

Sci Rep

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The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.

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Section: Discussionmentioning

confidence: 99%

Effective deep learning for oral exfoliative cytology classification

Sukegawa

Tanaka

Nakano

et al. 2022

Sci Rep

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“…The measured values are (Recall = 0.9272, F1-Score = 0.8008, and Precision = 0.7384) and (Recall = 0.9343, F1-Score = 0.6785, and Precision = 0.5931) for the MoNuSeg and TNBC Datasets, respectively Limitations: More training data fed to the network is required to improve accuracy. In order to increase generalization and avoid being overfit, the network needs more training data Year: 2021 Ke et al ( 2021 ) proposed a computer-aided Cytology Image Diagnostic System of abnormalities in gynaecologic cytopathology Features: Backbone: ResNet-50 Loss: Cross-entropy Adam's optimizer was employed. The overall architecture of the proposed system entailed five important functional components: the segmentation model, the classification model, the spatial correlation model, the nuclear area correction model, and the aggregation model Comparison: U-Net, Mask Regional Convolutional Neural Network (Mask R-CNN), U-Net + + Datasets: They privately collected and manually annotated dataset of 130 cytological whole-slide images from Shanxi Tumour Hospital (Kather et al 2019 ) Parameters: Pixel Accuracy, Mean Pixel Accuracy and Mean IoU Inference: The proposed model was better in terms of performance as compared to other models, highlighting that the proposed methods were accomplished to effectively extract, interpret, and quantify morphological features.…”

Section: Survey On Deep Learning Based Nucleus Segmentationmentioning

confidence: 99%

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

et al. 2023

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Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.

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“…Matsuo et al [ 27 ] compared the performance of DL models in survival analysis for women with newly diagnosed cervical cancer with conventional Cox proportional hazard regression (CPH) models. Ke et al [ 28 ] proposed a DL diagnostic system that can distinguish high grade squamous intraepithelial lesion (HSIL), squamous cell carcinoma, atypical squamous cells of undetermined significance (ASCUS) and low grade squamous intraepithelial lesion. Wu et al [ 29 ] introduced automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Wang

Lee

Chang

et al. 2022

Cancers

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Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p<0.005).

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Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning

Cited by 13 publications

References 29 publications

Effective deep learning for oral exfoliative cytology classification

Effective deep learning for oral exfoliative cytology classification

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

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