Transfer learning for multi-center classification of chronic obstructive pulmonary disease

Understand

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans.

• Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions.
• However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols.
• To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions.