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Related Experiment Video

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Use of Ultra-high Field MRI in Small Rodent Models of Polycystic Kidney Disease for In Vivo Phenotyping and Drug Monitoring
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Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and

Yu Kurata1, Imari Mimura2,3, Satoshi Kodera4

  • 1Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan.

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Summary

Pathology foundation models combined with multiple instance learning (MIL) show superior kidney disease diagnosis performance. These models accurately identify relevant structures and outperform traditional methods in both internal and external validation studies.

Keywords:
Artificial intelligenceFoundation modelMultiple instance learningRenal pathology

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Area of Science:

  • Digital Pathology
  • Artificial Intelligence in Medicine
  • Renal Pathology

Background:

  • Pathology foundation models, pre-trained on extensive datasets, excel in various tasks.
  • Multiple instance learning (MIL) is a machine learning approach suitable for analyzing large image data like whole slide images (WSIs).

Purpose of the Study:

  • To evaluate the effectiveness of pathology foundation models integrated with MIL for kidney pathology analysis.
  • To compare the diagnostic performance of foundation models against traditional methods like ResNet50.

Main Methods:

  • Utilized 242 kidney WSIs from KPMP and Japan-Pathology AI Diagnostics Project for development, including healthy controls, acute interstitial nephritis, and diabetic kidney disease (DKD).
  • Employed pretrained pathology foundation models as patch encoders, comparing them with ImageNet-pretrained ResNet50.
  • Trained MIL models using extracted patch features for diagnostic classification and validated on an external cohort of 83 WSIs.

Main Results:

  • Foundation models achieved an area under the receiver operating characteristic curve (AUROC) > 0.980 in internal validation, significantly outperforming ResNet50.
  • In external validation, foundation models maintained high performance, while ResNet50's performance declined.
  • Attention heatmaps confirmed foundation models' ability to identify diagnostically relevant kidney structures.
  • Foundation models also demonstrated superior performance in predicting overt proteinuria.

Conclusions:

  • Integrating pathology foundation models with MIL provides robust diagnostic capabilities for kidney pathology.
  • Foundation models offer a significant advancement over traditional methods for kidney disease analysis, showing resilience in external validation.