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A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep

Naji Khosravan1, Haydar Celik2, Baris Turkbey2

  • 1Center for Research in Computer Vision, University of Central Florida, FL, United States.

Medical Image Analysis
|November 7, 2018
PubMed
Summary

This study introduces a collaborative computer-aided diagnosis (C-CAD) system that integrates eye-tracking with realistic radiology settings. The C-CAD system enhances radiologists' diagnostic accuracy by analyzing gaze patterns and using deep learning for simultaneous segmentation and diagnosis.

Keywords:
AttentionEye-trackingGraph sparsificationLung cancer screeningMulti-task deep learningProstate cancer screening

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Computer-aided diagnosis (CAD) tools aim to minimize radiologist errors like missed tumors.
  • Eye-tracking studies analyze radiologist visual search but often lack realistic settings.
  • Realistic radiology environments are crucial for effective tool development and validation.

Purpose of the Study:

  • To develop a novel collaborative CAD (C-CAD) system unifying CAD and eye-tracking in realistic radiology settings.
  • To enhance diagnostic accuracy and efficiency by analyzing radiologist gaze patterns.
  • To create a paradigm shift in how CAD systems interact with radiologists.

Main Methods:

  • Developed an eye-tracking interface simulating a real radiology reading room experience.
  • Proposed a novel graph-based clustering and sparsification algorithm for quantitative gaze pattern analysis.
  • Integrated a deep learning algorithm within a multi-task learning platform for simultaneous segmentation and diagnosis.

Main Results:

  • The C-CAD system demonstrated efficiency, accuracy, and applicability in a lung cancer screening experiment using low-dose chest CTs.
  • The system successfully processed radiologist gaze patterns to improve search efficiency.
  • The framework showed generalizability to other applications like prostate cancer screening with mp-MRI.

Conclusions:

  • The C-CAD system represents a significant advancement in integrating eye-tracking technology with CAD for realistic radiology environments.
  • The proposed methods offer a robust framework for analyzing visual search patterns and improving diagnostic decisions.
  • The C-CAD system holds promise for improving cancer screening accuracy and applicability across various imaging modalities.