Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[Hierarchical attention-driven multiple instance learning for clear cell renal cell carcinoma grading and staging in digital pathology].
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Xu Jianing, Mao Yixiao, Zhang Yu
Аннотация
OBJECTIVES: To develop a hierarchical attention-driven multiple instance learning (HA-MIL) framework for precise grading and staging of clear cell renal cell carcinoma (ccRCC).
METHODS: Whole-slide images (WSIs) and the corresponding clinicopathological labels were obtained from 504 patients from the TCGA-KIRC database. The proposed HA-MIL framework processes WSIs by segmenting them into patches, leverages a hierarchical attention mechanism to discern the significance of instances across different levels and incorporates a dynamic gated fusion module to refine feature aggregation. This approach mitigates the dependency on subjective evaluations by pathologists.
RESULTS: Experimental results showed that HA-MIL achieved an accuracy of (87.35±0.72)%, an AUC of 0.95, and an F1-score of (86.41±0.63)% in the staging task. For the grading task, the model attained an accuracy of (85.82±0.60)%, an AUC of 0.93, and an F1-score of (86.00±0.65)%. HA-MIL demonstrated significantly higher accuracy and robustness compared to the baseline methods including SVM, DSMIL, and ABMIL. Ablation studies confirmed the contribution of the hierarchical attention mechanism, which improved the accuracy and F1-score by 3.2% and 2.2%, respectively, outperforming the conventional attention mechanisms.
CONCLUSIONS: The HA-MIL framework exhibits superior performance for grading and staging of ccRCC, thus offering a novel, intelligent method for pathological image analysis of ccRCC in the clinical setting.
METHODS: Whole-slide images (WSIs) and the corresponding clinicopathological labels were obtained from 504 patients from the TCGA-KIRC database. The proposed HA-MIL framework processes WSIs by segmenting them into patches, leverages a hierarchical attention mechanism to discern the significance of instances across different levels and incorporates a dynamic gated fusion module to refine feature aggregation. This approach mitigates the dependency on subjective evaluations by pathologists.
RESULTS: Experimental results showed that HA-MIL achieved an accuracy of (87.35±0.72)%, an AUC of 0.95, and an F1-score of (86.41±0.63)% in the staging task. For the grading task, the model attained an accuracy of (85.82±0.60)%, an AUC of 0.93, and an F1-score of (86.00±0.65)%. HA-MIL demonstrated significantly higher accuracy and robustness compared to the baseline methods including SVM, DSMIL, and ABMIL. Ablation studies confirmed the contribution of the hierarchical attention mechanism, which improved the accuracy and F1-score by 3.2% and 2.2%, respectively, outperforming the conventional attention mechanisms.
CONCLUSIONS: The HA-MIL framework exhibits superior performance for grading and staging of ccRCC, thus offering a novel, intelligent method for pathological image analysis of ccRCC in the clinical setting.