Abstract:
Can smartphone imaging hardware act as a substitute for WSI
scanners in automated computational pathology?
How much (if any) performance degradation is observed in deep
learning algorithms when they are trained on smartphone acquired
images instead of WSI scanned images.
Slides from 12 cases of Oral Squamous Cell Carcinoma (OSCC) were
scanned using both a Leica/Aperio CS2 WSI scanner and a
smartphone placed at the eyepiece of a clinical microscope. Three
features: Mitotic figures, Keratin Pearls and Epithelial regions were
then labelled by expert pathologists in both WSI and smartphone
acquired images. Deep learning algorithms were then trained and
evaluated on both types of images for performance comparison.
The largest degradation in performance was observed in mitosis
detection (smartphone F1 score = 0.6437 Vs. WSI F1 score = 0.7059).
In Keratin pearl detection, algorithms trained on smartphone images
outperformed algorithms trained on WSI images (smartphone F1
score = 0.7312 Vs. WSI F1 score = 0.6984). Performance degradation
was minor for Epithelium segmentation (smartphone F1 score =
0.7631 Vs. WSI F1 score = 0.7846).
