ArticleCell reports. Medicine2023
Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- AI caption generation model for digital pathology of adenocarcinoma in endoscopic histopathology using multi-instance attention mechanisms.Scientific reports · 2026Article
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review.BMC cancer · 2025Article
- FC-YOLO: a fast inference backbone and lightweight attention mechanism-enhanced YOLO for detecting gastric adenocarcinoma in pathological image.Frontiers in oncology · 2025Article
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- [The model transferability of AI in digital pathology : Potential and reality].Pathologie (Heidelberg, Germany) · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
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16 authors.
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Abstract
Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.
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