Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
Fig 7
Interpreting the fine-tuned ResNet-50 model.
Class activation maps for representative cell types, highlighting the cell features that allow the Phase II network to classify each cell as either deformable or non-deformable sRBC. These heat maps are a measure of the model’s attention [51, 52], where red corresponds to the highest activation, i.e. attention. Top rows show the original images, while the bottom rows show activation heat maps. (A-C) correspond to the deformable sRBC class, while (D-F) correspond to the non-deformable sRBC class. For each panel consisting of cell images and class activation maps, the first column represent the original cell image with no implemented data augmentation. The next column, however, is the same cell image with additional data augmentations like reflection and rotation. The last 2 columns for each panel contain single cell images intentionally modified to remove certain regions (black blocks) in order to confuse the network. The number in each panel is the probability assigned by the network of the cell being a deformable (A-C) or a non-deformable (D-F) sRBC. For the deformable sRBCs in (A-C), the network still classifies accurately when part of the dimple is blocked, but the probability drops when the entire dimple is blocked. Hence for these types of cells the dimple is the key distinguishing feature. Analogously for the non-deformable sRBC cell in (D-F) the network needs to see at least one sharp endpoint or majority of the edge to classify reliably.