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Related Experiment Video

Updated: Feb 21, 2026

Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice

Automated Measurement of Pulmonary Emphysema and Small Airway Remodeling in Cigarette Smoke-exposed Mice

Published on: January 16, 2015

GENERATIVE METHOD TO DISCOVER EMPHYSEMA SUBTYPES WITH UNSUPERVISED LEARNING USING LUNG MACROSCOPIC PATTERNS (LMPS):

Jingkuan Song¹, Jie Yang¹, Benjamin Smith²

¹Department of Biomedical Engineering, Columbia University, New York, NY, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 10, 2017

Summary

This summary is machine-generated.

This study introduces unsupervised learning to discover new patterns in emphysema, a lung disease. These novel lung macroscopic patterns (LMPs) are reproducible and can predict existing subtypes of emphysema.

Keywords:

COPD CT Emphysema LDA Lung classification texture unsupervised learning

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Published on: September 19, 2025

Area of Science:

Radiology
Pulmonary Medicine
Computational Pathology

Background:

Pulmonary emphysema is closely linked to chronic obstructive pulmonary disease (COPD).
Traditional emphysema classification includes centrilobular (CLE), panlobular (PLE), and paraseptal (PSE) subtypes.
Supervised learning for emphysema classification relies on existing subtype definitions.

Purpose of the Study:

To explore unsupervised learning for discovering novel radiological emphysema subtypes.
To identify and validate lung macroscopic patterns (LMPs) as potential new emphysema subtypes.
To assess the reproducibility and predictive ability of discovered LMPs.

Main Methods:

Utilized a variant of the Latent Dirichlet Allocation (LDA) model.
Applied unsupervised learning to texture patterns in emphysematous lung regions.
Evaluated LMPs for reproducibility across different learning sets.

Main Results:

Discovered highly reproducible lung macroscopic patterns (LMPs).
Demonstrated that LMPs can predict traditional emphysema subtypes (CLE, PLE, PSE).
Showcased the potential of unsupervised learning for emphysema subtyping.

Conclusions:

Unsupervised discovery of LMPs offers a novel approach to emphysema classification.
LMPs show promise as objective, data-driven subtypes of pulmonary emphysema.
This method could refine our understanding and diagnosis of emphysema within COPD.