FIGURE 4. Deep learning classification of RI patches. (a) The
architecture of the CNN classifier for RI patches. (b) The loss curve
for the training set and the validation sets during 200 epochs of
training. (c)-(d) validation and test confusion matrices.
3.3 Whole-slide Analysis of Thrombus Composition
To further assess the performance of the method in analyzing whole-slide
images, the class of each patch was identified and stitched together to
create a large field-of-view thrombus RI image. The patch classification
results were used to create a virtually colored patch image and an
unstained image, which were compared to the ground truth annotation map
based on H&E-stained images. Figure 5 shows the results for four
different regions, including slides 1, 2, and 3, and a region of
interest (ROI) from slide 3. Despite the differences in resolution due
to the patch-wise processing, the prediction based on DL and ODT showed
a similar spatial distribution of RBC and fibrin regions as the ground
truth. The sizes of the regions varied from 101 × 334
μm2 for the ROI to 2.71 × 5.58 mm2for slide 1.