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A multi-task learning model for evaluating non-tumor gastric diseases indicators in whole slide images
Mingxi Fu (Tsinghua Shenzhen International Graduate School, Tsinghua University), Liming Liu (Southern University of Science and Technology, Shenzhen Third People’s Hospital), Fanglei Fu (Tsinghua Shenzhen International Graduate School, Tsinghua University), Jingli Ouyang (Tsinghua Shenzhen International Graduate School, Tsinghua University), Xueying Shi (Peking University Third Hospital), Song Duan (Chongqing University), Tian Guan (Tsinghua Shenzhen International Graduate School, Tsinghua University), Yonghong He (Tsinghua Shenzhen International Graduate School, Tsinghua University), Zhiqiang Cheng (Southern University of Science and Technology, Shenzhen Third People’s Hospital), Lianghui Zhu (Tsinghua Shenzhen International Graduate School, Tsinghua University)
Résumé : 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.