Computer-Aided diagnosis of closely related diseases

Abhinav Dhere

Abstract

It is often observed that certain human diseases exhibit similarities in some form while having different prognoses and requiring treatment strategies. These similarities may be in the form of risk factors towards the diseases, symptoms observed, visual similarity in imaging studies, or in some cases, similarity in molecular associations. Computer-Aided Diagnosis (CAD) of closely related diseases is challenging and requires tailored approaches to discriminate between such closely related diseases accurately. This thesis looks at two sets of closely related diseases of two different organs, identified from two different modalities. It develops novel approaches to achieve explainable and accurate CAD for these two close diseases. These two problems are discrimination of healthy, mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) from brain MRI-derived surface mesh and classifying healthy, non-COVID pneumonia and COVID from chest X-ray images. In the first part of this thesis, we present a novel 2D image representation for the brain mesh surface, called a height map. Further, we explore the use of height maps towards the hierarchical classification of healthy, MCI, and AD cases. We also compare different strategies of extracting features and regions of interest from height maps and their performance towards healthy vs. MCI vs. AD classification. We demonstrate that the proposed method achieves fast classification of AD and MCI with minor loss of accuracy compared to state of the art. In the second half of this thesis, we present a novel deep learning architecture called Multi-scale Attention Learning Residual Learning (MARL) and a new conicity loss for training the MARL architecture. We utilize MARL and the conicity loss for achieving hierarchical classification of normal, non-COVID pneumonia and COVID pneumonia from Chest X-ray images. We present classification results on three public datasets and demonstrate that the proposed method achieves comparable or marginally better performance than state-of-the-art in all cases. Further, we demonstrate that the proposed framework achieves clinically consistent explanations with extensive experimentation. Qualitatively, this is shown by comparing GradCAM heatmaps for the proposed method to those for the state-of-the-art method. It is observed that the heatmaps overlap better with the bounding boxes for pneumonia marked by experts compared to the overlap achieved by the state-of-the-art method. Next, we show quantitatively that the GradCAM heatmaps for the proposed method generally lie within inner regions of the lung for nonCOVID pneumonia. However, the same heatmaps lie in outer regions in the case of COVID pneumonia. Thus, we establish the clinical consistency of explanations provided by the proposed framework.

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