ReviewBioMed research international2021
A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development.
Review in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.Journal of imaging · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.Journal of multidisciplinary healthcare · 2024Article
- Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer.Artificial intelligence review · 2023Article
- A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.Artificial intelligence review · 2023Article
- Pulse Signal Analysis Based on Deep Learning Network.BioMed research international · 2022Article
- Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation.BioMed research international · 2022Article
- Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.BioMed research international · 2022Article
- A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer.Frontiers in neuroscience · 2022Article
- GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion.Frontiers in oncology · 2022Article
- The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.Computational and structural biotechnology journal · 2022Review
- Upregulated Circular RNAs in Gastric Cancer: Target Identification and Tools for Therapeutic Intervention.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.