Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
Fig 5
Phase I network performance metrics.
(A) Two examples of typical input image tiles for the Phase I network, along with the corresponding manually labeled segmentation mask assigning each pixel in the image to one of three pixel classes (listed on the right). A(i) shows a tile with deformable sRBCs and non-functionally adhered / other objects, while A(ii) shows one with a non-deformable sRBC and other object. (B) (i) Training and validation history of the total cross entropy / Jaccard loss function for the Phase I network. The solid curve corresponds to the average loss over 5 folds, while the same colored light band denotes the spread (standard deviation) in the loss over these folds. Training history is shown in red and validation in blue (purple indicates overlap). (ii) Final 5-fold averaged performance metric values for both training and validation reached by our Phase I network at the end of training over 50 epochs. Uncertainties indicate spread around the mean of each metric over the 5 folds.