  Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by
infection with Aspergillus. Computed tomography (CT) images are frequently
requested in patients with suspected and established disease, but the
radiological signs on CT are difficult to quantify making accurate follow-up
challenging. We propose a novel method to train Convolutional Neural Networks
using only regional labels on the presence of pathological signs, to not only
detect CPA, but also spatially localize pathological signs. We use average
intensity projections within different ranges of Hounsfield-unit (HU) values,
transforming input 3D CT scans into 2D RGB-like images. CNN architectures are
trained for hierarchical tasks, leading to precise activation maps of
pathological patterns. Results on a cohort of 352 subjects demonstrate high
classification accuracy, localization precision and predictive power of 2 year
survival. Such tool opens the way to CPA patient stratification and
quantitative follow-up of CPA pathological signs, for patients under drug
therapy.
