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Article
Open AccessPrediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT
To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict dis...
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Chapter and Conference Paper
Abstract: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection using Contrastive Representations
Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic obstructive pulmonary disease (COPD) is a prime example, being underdiagno...
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Chapter and Conference Paper
Contrastive Representations for Unsupervised Anomaly Detection and Localization
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring labels during training. Often, this is achieved by learning a data distribution of normal sam...
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Chapter and Conference Paper
cOOpD: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection Using Contrastive Representations
Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagno...
