Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Ba, Wei; Wang, Shuhao; Shang, Mei; Zhang, Ziyan; Wu, Huan; Yu, Chunkai; Xing, Ranran; Wang, Wenjuan; Wang, Lang; Liu, Cancheng; Shi, Huaiyin; Song, Zhigang

doi:10.1038/s41379-022-01073-z

Cited by 40 publications

(16 citation statements)

References 30 publications

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“…In recent years, artificial intelligence has achieved unprecedented development, and the application of this frontier technology in the field of medicine has gradually become a new trend. Recent studies have demonstrated promising results of deep learning algorithms in recognizing various lesions using WSIs ( 12 , 14 , 15 , 21 , 22 ). As for EC, the increasing diagnostic workload of endometrial biopsy specimens calls for the development of new models with high sensitivity and specificity.…”

Section: Discussionmentioning

confidence: 99%

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

Zhang

Ba²,

Zhao³

et al. 2022

Front. Oncol.

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The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists’ confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

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Section: Discussionmentioning

confidence: 99%

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

Zhang

Ba²,

Zhao³

et al. 2022

Front. Oncol.

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“…At this time, most of the proposed computational pathology tools are restricted to research use only (RUO) and can only be used to provide a complementary analysis to that performed by a pathologist. In [40], [41], it was shown that the combination of computational pathology and human pathologists has the potential to improve accuracy and efficiency in gastric cancer diagnosis. However, with the use of computational pathology, remarkable progress has been made beyond RUO.…”

Section: Computational Pathologymentioning

confidence: 99%

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

Brixtel¹,

Bougleux

Lézoray

et al. 2022

IEEE Access

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With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital revolution. Simultaneously, with the development of image analysis algorithms based on artificial intelligence tools, the application of computerized WSI analysis can now be expected. However, transferring such tools into clinical practice is very challenging as they must deal with many artifacts that can occur during sample preparation and digitization. Therefore, the quality of WSIs is of prime importance, and we propose a review of the state-of-the-art of computational approaches for quality control. In particular, we focus on WSI quality issues related to the presence of sample preparation artifacts, compression artifacts, color variations, and out-of-focus areas. An analysis of the monthly WSI clinical routine in a cytological laboratory confirms the importance of implementing quality control measures. Given this observation, we draw perspectives on how a computational quality process can be included in a computational pathology diagnosis pipeline.

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“…However, unrelated features and redundancy were still observed. A case study was conducted to assess the pathologist’s deep learning competence in diagnosing gastric infections [ 22 ]. In this study, 16 professional pathologists inferred a total of 110 whole slide images (WSI) containing 50 malignant and 60 benign tumors with and without deep learning assistance.…”

Section: Introductionmentioning

confidence: 99%

GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification

et al. 2022

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In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model’s hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy.

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Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

Cited by 40 publications

References 30 publications

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning

Whole Slide Image Quality in Digital Pathology: Review and Perspectives

GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification

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