Fig. 4
From: Fast and accurate view classification of echocardiograms using deep learning
Deep learning model simultaneously distinguishes among 15 standard echocardiographic views. We developed a deep-learning method to classify among standard echocardiographic views, represented here by t-SNE clustering analysis of image classification. On the left, t-SNE clustering of input echocardiogram images. Each image is plotted in 4800-dimensional space according to the number of pixels, and projected to two-dimensional space for visualization purposes. Different colored dots represent different view classes (see legend in figure). Prior to neural network analysis, input data does not cluster into clear groups. On the right, data as processed through the last fully connected layer of the neural network are again represented in two-dimensional space, showing organization into clusters according to view category. Abbreviations: a4c apical 4 chamber, psla parasternal long axis, saxbasal short axis basal, a2c apical 2 chamber, saxmid short axis mid/mitral, a3c apical 3 chamber, sub4c subcostal 4 chamber, a5c apical 5 chamber, ivc subcostal ivc, rvinflow right ventricular inflow, supao suprasternal aorta/aortic arch, subao subcostal/abdominal aorta, cw continuous-wave Doppler, pw pulsed-wave Doppler, mmode m-mode recording
