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Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning.

Jordan Fuhrman¹, Rowena Yip², Yeqing Zhu²

¹Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, 60637, USA. jdfuhrman@uchicago.edu.

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|January 21, 2023

Summary

A new computer-aided diagnosis system accurately detects emphysema on low-dose CT scans. This automated approach using Transfer AMIL deep learning shows high performance and potential for clinical use in thoracic imaging.

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Low-dose CT (LDCT) scans for lung cancer screening can reveal other thoracic abnormalities like emphysema.
Manual assessment of LDCT scans for emphysema is time-consuming and subjective.
Automated systems are needed for efficient and objective emphysema detection.

Purpose of the Study:

To develop and evaluate an automatic computer-aided diagnosis (CAD) system for emphysema detection using LDCT scans.
To assess the performance of a novel Transfer AMIL deep learning approach for emphysema classification.
To investigate the interpretability of the developed model.

Main Methods:

Utilized 865 low-dose CT scans (LDCTs).
Developed a Transfer AMIL (Attention-based Multiple Instance Learning) deep learning model.
Evaluated the model's performance in classifying scans with and without emphysema.

Main Results:

The Transfer AMIL approach achieved an area under the ROC curve of 0.94 ± 0.04.
This performance was a statistically significant improvement over other evaluated methods.
Attention weight analysis revealed prioritization of upper lung regions, providing model interpretability.

Conclusions:

The novel Transfer AMIL method demonstrates high accuracy for emphysema detection in LDCT scans.
The system provides interpretable insights by highlighting influential image regions.
This automated approach shows significant potential for clinical integration in thoracic imaging analysis.