ArticleiScience2025
Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Mapping the genetic landscape of hereditary diffuse-type gastric cancer progression.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Innovative pathological and therapeutic approaches for poorly cohesive gastric cancer.Frontiers in oncology · 2026Review
- The Changing Landscape of Hereditary Diffuse Gastric Cancer.Journal of gastric cancer · 2026Review
- FC-YOLO: a fast inference backbone and lightweight attention mechanism-enhanced YOLO for detecting gastric adenocarcinoma in pathological image.Frontiers in oncology · 2025Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Hereditary diffuse gastric cancer (HDGC) is a rare condition where early tumor detection is challenging due to diffuse infiltration and tumor heterogeneity. Accurate identification of DGC cells is essential for understanding tumor behavior. This study aimed to develop deep learning models to automatically detect key tumor cell types-typical and atypical signet ring cells and non-signet ring tumor cells-in H&E-stained digital pathology slides from HDGC patients. Using a multi-center dataset of 350 whole-slide images and over 91,000 annotated cells from 43 patients, we trained nnU-Net models for cell detection and compared them to Faster R-CNN baselines. We also conducted a reader study with five pathologists to benchmark performance. nnU-Net outperformed both pathologist inter-observer agreement and Faster R-CNN, achieving an F
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