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Related Experiment Video

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Predicting clinical endpoints and visual changes with quality-weighted tissue-based renal histological features.

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  • 1Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.

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Summary

Weak annotations significantly improve multi-instance learning (MIL) for digital histopathology, enhancing performance in classifying renal biopsies. This scalable approach aids clinical decision-making by highlighting relevant tissue features.

Keywords:
Bayesian Neural Network (BNN)computer visiondigital histopathologykidney transplantmulti-instance learning

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Area of Science:

  • Digital pathology
  • Computational biology
  • Medical image analysis

Background:

  • Data-driven algorithms in digital histopathology face challenges from limited expert annotations and dataset diversity.
  • Multi-instance learning (MIL) addresses annotation scarcity in whole slide images (WSI) but often underperforms full supervision.

Purpose of the Study:

  • To enhance the effectiveness and scalability of MIL in digital histopathology by incorporating weak annotations.
  • To develop an analysis framework for renal biopsy slides (PAS and SR) to predict clinical outcomes.

Main Methods:

  • A novel framework was developed to segment renal tissues and extract combined handcrafted and deep features.
  • A soft attention model integrated these features for predicting slide-level labels like delayed graft function (DGF) and acute tubular injury (ATI).
  • A tissue segmentation quality metric was introduced to mitigate segmentation errors, and the model was trained using 5-fold cross-validation.

Main Results:

  • The soft attention model achieved an average ROC-AUC of , outperforming ResNet50 (), handcrafted features (), and baseline methods ().
  • Weighting tissues by segmentation quality further improved performance by .
  • The approach demonstrated its utility in supporting clinical decisions through intuitive visualizations pinpointing relevant tissues.

Conclusions:

  • Integrating weak annotations significantly boosts MIL performance in digital histopathology, offering a scalable solution.
  • The developed framework and attention mechanism provide accurate predictions for renal biopsy classification.
  • The method's ability to visualize contributing tissue regions supports clinical decision-making in pathology.