A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Ai, Shiliang; Li, Chen; Li, Xiaoyan; Jiang, Tao; Grzegorzek, Marcin; Sun, Changhao; Rahaman, Md Mamunur; Jing-hua, Zhang; Yao, Yu-Dong; Liang, Hong

doi:10.1155/2021/6671417

Cited by 30 publications

(16 citation statements)

References 92 publications

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“…In recent years, with the maturity of deep learning technology, machine learning has gradually been applied in various areas of the medical field, such as precise cell classification [ 10 ] and image analysis for pathology [ 11 , 12 ]. In addition, human pose estimation has broad application prospects in computer vision, pattern recognition, video/image sequence processing, and other technologies [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

Chen

et al. 2022

BioMed Research International

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A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high-cost, and time-consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm’s accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients ( p < 0.01 ), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

Chen

et al. 2022

BioMed Research International

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“…Gastric cancer is a type of cancer caused by the immortal proliferation of abnormal cells in the stomach, and it is the fifth most common type of cancer all over the world ( 1 , 2 ), which seriously affects people’s health. Gastric cancer has a high morbidity and mortality rate and is the world’s third largest disease related to cancer deaths ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Wang

Liu

et al. 2022

Front. Oncol.

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BackgroundAnalysis of histopathological slices of gastric cancer is the gold standard for diagnosing gastric cancer, while manual identification is time-consuming and highly relies on the experience of pathologists. Artificial intelligence methods, particularly deep learning, can assist pathologists in finding cancerous tissues and realizing automated detection. However, due to the variety of shapes and sizes of gastric cancer lesions, as well as many interfering factors, GCHIs have a high level of complexity and difficulty in accurately finding the lesion region. Traditional deep learning methods cannot effectively extract discriminative features because of their simple decoding method so they cannot detect lesions accurately, and there is less research dedicated to detecting gastric cancer lesions.MethodsWe propose a gastric cancer lesion detection network (GCLDNet). At first, GCLDNet designs a level feature aggregation structure in decoder, which can effectively fuse deep and shallow features of GCHIs. Second, an attention feature fusion module is introduced to accurately locate the lesion area, which merges attention features of different scales and obtains rich discriminative information focusing on lesion. Finally, focal Tversky loss (FTL) is employed as a loss function to depress false-negative predictions and mine difficult samples.ResultsExperimental results on two GCHI datasets of SEED and BOT show that DSCs of the GCLDNet are 0.8265 and 0.8991, ACCs are 0.8827 and 0.8949, JIs are 0.7092 and 0.8182, and PREs are 0.7820 and 0.8763, respectively.ConclusionsExperimental results demonstrate the effectiveness of GCLDNet in the detection of gastric cancer lesions. Compared with other state-of-the-art (SOTA) detection methods, the GCLDNet obtains a more satisfactory performance. This research can provide good auxiliary support for pathologists in clinical diagnosis.

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“…The notable results include the identification of tumor regions such as skin (Andre et al, 2019 ), gastrointestinal (Song et al, 2020 ), and lung (Coudray et al, 2018 ), especially breast cancer pathological images have been extensively studied (Zhou et al, 2020 ). Compared to breast pathological images, there are few studies related to gastric cancer (Ai et al, 2021 ), especially the HER2 score evaluation of gastric cancer on IHC images. Additionally, WSIs have billions of pixels in an image, which poses computational challenges on Computer-Aided Diagnosis (CAD) systems.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer

et al. 2022

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Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.

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A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Cited by 30 publications

References 92 publications

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer

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