Classification of lung pathologies in neonates using dual-tree complex wavelet transform.

INTRODUCTION: Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. METHODS: We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. RESULTS: Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. CONCLUSION: Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

Interpretable Artificial Intelligence through Locality Guided Neural Networks.

In current deep learning architectures, each of the deeper layers in networks tends to contain hundreds of unorganized neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons using correlation as the criteria, humans can observe how clusters of neighbouring neurons interact with each other. Research in Explainable Artificial Intelligence (XAI) aims to all alleviate the black-box nature of current AI methods and make them understandable by humans. In this paper, we extend our previous algorithm for XAI in deep learning, called Locality Guided Neural Network (LGNN). LGNN preserves locality between neighbouring neurons within each layer of a deep network during training. Motivated by Self-Organizing Maps (SOMs), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. Our algorithm can be easily plugged into current state of the art Convolutional Neural Network (CNN) models to make the neighbouring neurons more correlated. A cluster of neighbouring neurons activating for a class makes the network both quantitatively and qualitatively more interpretable when visualized, as we show through our experiments. This paper focuses on image processing with CNNs, but can theoretically be applied to any type of deep learning architecture. In our experiments, we train VGG and WRN networks for image classification on CIFAR100 and Imagenette. Our experiments analyse different perceptible clusters of activations in response to different input classes.