Rotator Cuff Tears (RCTs) are primarily age-related musculoskeletal disorders affecting the shoulder region. In this paper, we present a research effort that aims to construct a Computer-Aided Diagnosis (CAD) model for RCTs. The model utilizes three-plane MRI slides coupled with diagnostic outcomes. To enhance model interpretability, we employed MRNet, conducting training on each anatomical plane, and subsequently merged the results using logistic regression. At the individual plane level and the fusion level, we applied Grad-CAM and SHapley Additive exPlanations (SHAP), respectively. Additionally, we introduce CAMscore, a method using Grad-CAM, designed to quantitatively determine the diagnostic relevance of individual MRI slides. When fusing the three planes, we achieved a maximum F1-score of 0.9508. Additionally, we observed a notably higher diagnostic efficacy in the sagittal plane compared to the axial and coronal planes. However, our study has certain limitations, including the need for greater dataset diversity and the necessity for verification by medical professionals. Nevertheless, our study advances the field of CAD by improving the understanding of the decision-making processes of models utilizing three-plane fusion.
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made by Convolutional Neural Networks (CNNs), facilitating qualitative insight into these neural networks when they are, for instance, used for the purpose of medical image analysis. In this paper, we investigate to what extent CAM also enables a quantitative understanding of CNN-based classification models through the creation of segmentation masks out of class activation maps, hereby targeting the use case of brain tumor classification. To that end, when a class activation map has been created for a correctly classified brain tumor, we additionally perform tumor segmentation by binarization of the aforementioned map, leveraging different methods for thresholding. In a next step, we compare this CAM-based segmentation mask to the segmentation ground truth, measuring similarity through the use of Intersection over Union (IoU). Our experimental results show that, although our CNN-based classification models have a similarly high accuracy between 86.0% and 90.8%, their generated masks are different. For example, our Modified VGG-16 model scores an mIoU of 12.2%, whereas AlexNet scores an mIoU of 2.1%. When comparing with the mIoU obtained by our U-Net-based models, which is between 66.6% and 67.3%, and where U-Net is a dedicated pixel-wise segmentation model, our experimental results point to a significant difference in terms of segmentation effectiveness. As such, the use of CAM for the purpose of proxy segmentation or as a ground truth segmentation mask generator comes with several limitations.
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