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<p>Chronic kidney disease (CKD) affects nearly 10% of the global population, where accurate pathological diagnosis and lesion grading are critical for personalized treatment. Although multiple stainings of consecutive sections provides complementary diagnostic cues, cross-staining representation remains constrained by structural misalignment and pronounced heterogeneity.To address these challenges, we propose a matching-driven, spatially enriched multiple instance learning (SEMIL) framework. First, a hybrid feature matching strategy is employed to establish spatial correspondences between pathological entities across differently stained consecutive slices, thereby capturing cross-slice structural associations. Second, a hierarchical entity–context descriptor is introduced to enhance cross-staining pathology feature representation, incorporating spatial enrichment into multiple instance learning. Furthermore, clinical embedding is integrated as prior knowledge to improve case-level feature representation for pathological diagnosis. We constructed two datasets for CKD diagnosis and kidney tissue damage grading. Extensive experiments demonstrate that SEMIL consistently outperforms standard multiple instance learning baselines, achieving gains of up to 4–5%. Visualization further confirms the effectiveness of entity-level spatial modeling. The proposed framework substantially improves MIL-based pathological diagnosis and tissue damage grading in CKD, while offering a generalizable paradigm for case-level pathology AI with potential applicability across other subspecialties.</p>

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Cross-staining pathological diagnosis based on spatially enriched multiple instance learning with clinical embedding

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Qiming He,
  • Shuang Ge,
  • Qiang Huang,
  • Jing Li,
  • Tian Guan,
  • Zhe Wang,
  • Yonghong He

摘要

Chronic kidney disease (CKD) affects nearly 10% of the global population, where accurate pathological diagnosis and lesion grading are critical for personalized treatment. Although multiple stainings of consecutive sections provides complementary diagnostic cues, cross-staining representation remains constrained by structural misalignment and pronounced heterogeneity.To address these challenges, we propose a matching-driven, spatially enriched multiple instance learning (SEMIL) framework. First, a hybrid feature matching strategy is employed to establish spatial correspondences between pathological entities across differently stained consecutive slices, thereby capturing cross-slice structural associations. Second, a hierarchical entity–context descriptor is introduced to enhance cross-staining pathology feature representation, incorporating spatial enrichment into multiple instance learning. Furthermore, clinical embedding is integrated as prior knowledge to improve case-level feature representation for pathological diagnosis. We constructed two datasets for CKD diagnosis and kidney tissue damage grading. Extensive experiments demonstrate that SEMIL consistently outperforms standard multiple instance learning baselines, achieving gains of up to 4–5%. Visualization further confirms the effectiveness of entity-level spatial modeling. The proposed framework substantially improves MIL-based pathological diagnosis and tissue damage grading in CKD, while offering a generalizable paradigm for case-level pathology AI with potential applicability across other subspecialties.