A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

Li, Xintong; Li, Chen; Rahaman, Md Mamunur; Sun, Hongzan; Li, Xiaoqi; Wu, Jian; Yao, Yu-Dong; Grzegorzek, Marcin

doi:10.1007/s10462-021-10121-0

Cited by 104 publications

(36 citation statements)

References 228 publications

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“…The selection of suitable classifiers is the primary problem of ensemble learning, and after relevant experiments in the complementarity comparison experimental platform, it can be observed that these classifiers exhibit different performances ( 43 ). The complementarity possessed by these classifiers can adequately meet the needs of ensemble learning ( 44 ).…”

Section: Discussionmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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IntroductionGastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. Therefore, this paper compares the performance of multiple algorithms in anticipation of applying ensemble learning to a practical gastric cancer classification problem.MethodsThe complementarity of sub-size pathology image classifiers when machine performance is insufficient is explored in this experimental platform. We choose seven classical machine learning classifiers and four deep learning classifiers for classification experiments on the GasHisSDB database. Among them, classical machine learning algorithms extract five different image virtual features to match multiple classifier algorithms. For deep learning, we choose three convolutional neural network classifiers. In addition, we also choose a novel Transformer-based classifier.ResultsThe experimental platform, in which a large number of classical machine learning and deep learning methods are performed, demonstrates that there are differences in the performance of different classifiers on GasHisSDB. Classical machine learning models exist for classifiers that classify Abnormal categories very well, while classifiers that excel in classifying Normal categories also exist. Deep learning models also exist with multiple models that can be complementarity.DiscussionSuitable classifiers are selected for ensemble learning, when machine performance is insufficient. This experimental platform demonstrates that multiple classifiers are indeed complementarity and can improve the efficiency of ensemble learning. This can better assist doctors in diagnosis, improve the detection of gastric cancer, and increase the cure rate.

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Section: Discussionmentioning

confidence: 99%

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Chen

Liu

et al. 2022

Front. Med.

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“…In this study, we used pathological images artificially generated by pathologists to ensure that the selected areas were dominated by tumor components. Due to objective conditions, this study did not use WSI as the information source for pathological images [ 42 ]. Modeling based on pathological staining images is significantly worse than modeling based on enhanced MRI data.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer

Zhao

2022

BioMed Research International

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Objective. Image texture information was extracted from enhanced magnetic resonance imaging (MRI) and pathological hematoxylin and eosin- (HE-) stained images of female breast cancer patients. We established models individually, and then, we combine the two kinds of data to establish model. Through this method, we verified whether sufficient information could be obtained from enhanced MRI and pathological slides to assist in the determination of epidermal growth factor receptor (EGFR) mutation status in patients. Methods. We obtained enhanced MRI data from patients with breast cancer before treatment and selected diffusion-weighted imaging (DWI), T1 fast-spin echo (T1 FSE), and T2 fast-spin echo (T2 FSE) as the data sources for extracting texture information. Imaging physicians manually outlined the 3D regions of interest (ROIs) and extracted texture features according to the gray level cooccurrence matrix (GLCM) of the images. For the HE staining images of the patients, we adopted a specific normalization algorithm to simulate the images dyed with only hematoxylin or eosin and extracted textures. We extracted texture features to predict the expression of EGFR. After evaluating the predictive power of each model, the models from the two data sources were combined for remodeling. Results. For enhanced MRI data, the modeling of texture information of T1 FSE had a good predictive effect for EGFR mutation status. For pathological images, eosin-stained images can achieve a better prediction effect. We selected these two classifiers as the weak classifiers of the final model and obtained good results (training group: AUC, 0.983; 95% CI, 0.95-1.00; accuracy, 0.962; specificity, 0.936; and sensitivity, 0.979; test group: AUC, 0.983; 95% CI, 0.94-1.00; accuracy, 0.943; specificity, 1.00; and sensitivity, 0.905). Conclusion. The EGFR mutation status of patients with breast cancer can be well predicted based on enhanced MRI data and pathological data. This helps hospitals that do not test the EGFR mutation status of patients with breast cancer. The technology gives clinicians more information about breast cancer, which helps them make accurate diagnoses and select suitable treatments.

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“…Reviewing the past 10 years, deep learning has seen a proliferation of improvements in models and algorithms, and availability and computational power of large-scale image data has continued to improve, solving many challenges in computer vision. In 2022, Li et al [ 85 ] provided a comprehensive summary of deep learning research in medical image classification, detection and segmentation, alignment, and retrieval; applications of DenseNet for medical images mainly include image classification, segmentation, detection, alignment, reconstruction, retrieval, generation, enhancement, and fusion, but research and improvement of its technology mainly focus on classification and recognition, segmentation, and localization detection; medical image classification commonly used application scenarios are disease diagnosis; medical image segmentation projects are mostly applied to segmentation of lesions and organs, such as brain segmentation, lung segmentation, heart segmentation, and liver segmentation. Therefore, this section summarizes applications of DenseNet in field of medical image analysis from these three aspects.…”

Section: Application Of Densenet In Medical Image Analysismentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

Cited by 104 publications

References 228 publications

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

A comparative study of gastric histopathology sub-size image classification: From linear regression to visual transformer

Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer

Dense Convolutional Network and Its Application in Medical Image Analysis

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