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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Neighborhood attention transformer multiple instance learning for whole slide image classification.

Rukhma Aftab1, Qiang Yan1,2, Juanjuan Zhao1

  • 1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China.

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Summary
This summary is machine-generated.

Neighborhood Attention Transformer Multiple Instance Learning (NATMIL) improves cancer diagnosis from whole slide images by analyzing tile context. This weakly supervised method enhances accuracy in classifying tumors, outperforming existing approaches.

Keywords:
attention transformerlung cancermultiple instance learningweakly supervised learningwhole slide images

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Digital pathology

Background:

  • Pathologists use whole slide images (WSIs) for cancer diagnosis, but deep learning models struggle with tumor heterogeneity.
  • Weakly supervised models classifying WSIs by tiles may produce false positives/negatives due to localized analysis.

Purpose of the Study:

  • To introduce NATMIL (Neighborhood Attention Transformer Multiple Instance Learning) for improved WSI-based cancer classification.
  • To leverage contextual dependencies among WSI tiles for more accurate tumor subtyping.

Main Methods:

  • Developed NATMIL, incorporating Neighborhood Attention Transformer to integrate tile context.
  • Enhanced multiple instance learning by considering broader tissue context within WSIs.

Main Results:

  • NATMIL demonstrated superior accuracy in subtyping non-small cell lung cancer (NSCLC) and lymph node (LN) tumors.
  • Achieved 89.6% accuracy on the Camelyon dataset and 88.1% on the TCGA-LUSC dataset.
  • Outperformed existing weakly supervised algorithms in quantitative analysis.

Conclusions:

  • NATMIL significantly improves tumor classification accuracy by reducing errors from isolated tile analysis.
  • Integrating contextual dependencies enhances precision in cancer diagnosis using WSIs.
  • NATMIL shows potential as a robust tool for digital pathology applications.