  We propose a deep learning clustering method that exploits dense features
from a segmentation network for emphysema subtyping from computed tomography
(CT) scans. Using dense features enables high-resolution visualization of image
regions corresponding to the cluster assignment via dense clustering activation
maps (dCAMs). This approach provides model interpretability. We evaluated
clustering results on 500 subjects from the COPDGenestudy, where radiologists
manually annotated emphysema sub-types according to their visual CT assessment.
We achieved a 43% unsupervised clustering accuracy, outperforming our baseline
at 41% and yielding results comparable to supervised classification at 45%. The
proposed method also offers a better cluster formation than the baseline,
achieving0.54 in silhouette coefficient and 0.55 in David-Bouldin scores.
