ArticleScientific reports2025
A multi-task learning model for evaluating non-tumor gastric diseases indicators in whole slide images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Radiomics and deep learning in upper tract urothelial carcinoma: advancing preoperative risk stratification and clinical decision-making.Frontiers in oncology · 2026Review
- Article
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Authors and funding
10 authors.
Funding
Abstract
Inflammation, acute activity, intestinal metaplasia, and atrophy are key indicators in gastric biopsy evaluations, with their grading being crucial for assessing gastric cancer progression. However, diagnostic subjectivity among pathologists and the complex interrelationships between these indicators present significant challenges. Additionally, the high resolution of whole slide images (WSIs) complicates large-scale annotation efforts. To address these issues, we propose a multi-task learning model utilizing self-supervised pre-trained weights from extensive pathological datasets. The model integrates four indicators-severity of inflammation, atrophy, acute activity, and intestinal metaplasia-training on WSIs to predict these indicators while accounting for their interrelationships. Our results show that multi-task learning outperforms single-task models, achieving higher accuracy across all indicators. This model can thus serve as an auxiliary tool for evaluating non-tumor gastric diseases and supporting diagnosis.
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Registered trials
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