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An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading.

Pierluigi Carcagnì1, Marco Leo1, Luca Signore2

  • 1Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council (CNR), Via Monteroni snc University Campus, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

Advanced deep learning models, including transformers, significantly improve colorectal cancer (CRC) detection and grading from histological images. This AI approach enhances diagnostic accuracy and consistency for better treatment planning.

Keywords:
artificial intelligencecolon carcinomadeep learningensemblinghistopathology

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Digital pathology

Background:

  • Colorectal cancer (CRC) is a major global health concern, necessitating accurate grading for effective treatment planning.
  • Current manual grading methods for CRC are prone to observer variability, impacting treatment consistency.
  • The potential of advanced machine learning models in improving CRC grading remains underexplored.

Purpose of the Study:

  • To investigate the efficacy of deep learning models, specifically convolutional neural networks and transformer architectures, for colon carcinoma detection and grading.
  • To introduce and evaluate the use of transformer architectures and ensemble strategies for automated colon cancer diagnosis.
  • To establish a new state-of-the-art in automated colon cancer detection and grading using advanced AI.

Main Methods:

  • Systematic investigation of deep learning paradigms, including convolutional neural networks and transformer architectures.
  • Application of transformer architectures and ensemble strategies for colon cancer histological image analysis.
  • Validation on the largest publicly available dataset for colon carcinoma detection and grading.

Main Results:

  • Transformer architecture integration led to a 3% accuracy increase in colon cancer detection (two-class problem).
  • An ensemble strategy combined with transformer architecture improved grading accuracy by up to 4% (three-class problem).
  • Demonstrated substantial improvements over existing state-of-the-art methods on a large dataset.

Conclusions:

  • Advanced deep learning models, particularly transformers, show significant promise for enhancing automated colon cancer diagnosis.
  • The developed approach offers improved accuracy and consistency in both cancer detection and grading.
  • This study paves the way for more reliable AI-driven tools in clinical oncology for colorectal cancer management.