Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury

Abstract

Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.

Light sheet imaging and interactive analysis of the cardiac structure in neonatal mice

“…To further improve the efficiency and accuracy of interactive analysis, we have created machine learningbased approaches for the assessment of the ventricular myocardium [30,31]. Our interpretable neural network model, consisting of a VGG-19 network with gradientweighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) embedding methods, enables us to accurately extract and visualize features from a region of interest in our imaging data without explicitly defining them.…”

A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology

“…In terms of XAI methods, SHAP (twenty-five) and Grad-CAM (twenty) were the prevalent XAI choices for these studies, similarly to what found in the other papers employing XAI in combination with some kinds of evaluation (Figure 9). [172,181,[187][188][189] Grad-CAM ECG [190][191][192][193][194][195][196][197][198][199][200]] EHR [199] CMR [199] Others [187,192,[201][202][203][204][205] Grad-CAM++ ECG […”

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

“…For instance, Pérez-Pelegrıó et al 28 developed a new explainable approach that combines class activation mapping with U-net to automatically estimate the LV volume in end diastole and obtain the result in the form of a segmentation mask without segmentation labels to train the algorithm. Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,[48][49][50] or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”

“…The aims of these articles were diverse and included the automatic calculation of the LV volume and ejection fraction 28,35 ; the automatic segmentation of cardiac structures as LV/RV, myocardium, interventricular septal, and posterior LV wall 39,51,55 ; and classification approaches. Classification was investigated in the context of cardiac disorders such as mitral valve diseases, 48 myocardial injury, 50 coronary artery disease (CAD), 34,54 cardiomyopathy, 33 congenital heart disease, 40 and HF 32 ; even in newborn 49 for the presence of intracardiac devices (eg, catheters, pacemaker, and defibrillator leads) or motion 18 ; for severe left atrial dilation and LV hypertrophy 43 ; and for CMR image view. 27 In order to improve the interpretability of the segmentation/prediction/classification results, these articles generally focused on DL-based XAI methods as class activation mapping or Grad-CAM, SmoothGrad, saliency maps, testing with concept activation vectors, and guided backpropagation.…”

“…Grad-CAM was used in 7 cardiac imaging studies, either for classification 18,34,40,48–50 or segmentation. 51 The latter in particular proposed a new interpretable CNN model (fast and accurate echocardiographic automatic segmentation based on U-Net) that integrates U-net architecture and transfer learning (from Visual Geometry Group 19) to segment 2-dimensional echocardiography of 88 patients into 3 regions (LV, interventricular septal, and posterior LV wall).…”