Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning

Abstract

Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect diagnosis. From an application perspective, these models have exhibited two major challenges: (1) They require large volumes of annotated data at the training stage and (2) They lack explainable factors to justify their outcomes at the prediction stage. In the present study, we developed a class activation mapping (CAM)-based ensemble model, called Ensemble-CAM, to address both of these challenges via weakly supervised learning by employing explainable AI (XAI) functions. Ensemble-CAM utilizes class labels to predict the location of disease in association with interpretable features. The proposed work leverages ensemble and transfer learning with class activation functions to achieve three objectives: (1) minimizing the dependency on strongly annotated data when locating thoracic diseases, (2) enhancing confidence in predicted outcomes by visualizing their interpretable features, and (3) optimizing cumulative performance via fusion functions. Ensemble-CAM was trained on three CXR image datasets and evaluated through qualitative and quantitative measures via heatmaps and Jaccard indices. The results reflect the enhanced performance and reliability in comparison to existing standalone and ensembled models.

Keywords: class activation maps; computer aided diagnosis; ensemble learning; explainable artificial intelligence; transfer learning; weak supervised learning.

Figure 1

Extracting localization details via classification skills.

Figure 2

Block diagram of Ensemble-CAM for localizing abnormalities in the X-ray image with interpretable outcomes.

Figure 3

Ensemble-CAM generates localization information from image level class labels while making the process interpretable. Reference to labeled data, IoU has been computed for quantification of the result.

Figure 4

Estimation of bounding box annotation for cardiomegaly localization quantified by IoU scores.

Figure 5

Estimation of bounding box annotation for Pneumonia localization quantified by IoU scores.

Figure 6

Qualitative illustration of predicting bounding boxes for COVID-19 cases.