SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A Novel Approach To Train Convolutional Neural Networks On Lung CT Scans Using Binary Labels Only

Abstract

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 followup 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 RGBlike 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.

Using Artificial Intelligence in Fungal Lung Disease: CPA CT Imaging as an Example

“…We have shown [22,23] (Fig. 1) that a weakly supervised deep-learning framework is capable of detecting the presence of 3 types of CPA pathological signs with very high accuracy and specificity [23] and can potentially predict mortality within 5 years with high precision [22]. This presents a proof of concept that fungal lung infection may present a unique opportunity for the application of imaging AI to improve outcome.…”

“…We have shown [22,23] (Fig. 1) that a weakly supervised deep-learning framework is capable of detecting the presence of 3 types of CPA pathological signs with very high accuracy and specificity [23] and can potentially predict mortality within 5 years with high precision [22].…”

Unravelling machine learning: insights in respiratory medicine

“…is it a picture of a "cat"? ), was successfully trained on high-resolution computed tomography (CT) lung imaging to assess if images are from patients with chronic pulmonary aspergillosis (CPA) (yes/no) [1]. Input data consisted of CT scans ( pre-segmented to only show the lungs and transformed in maximum intensity projections to compress image information), and a label for each scan indicating if the subject has CPA or not.…”