White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection
Journal:
arXiv
Published Date:
May 8, 2025
Abstract
Colorectal cancer is one of the deadliest cancers today, but it can be
prevented through early detection of malignant polyps in the colon, primarily
via colonoscopies. While this method has saved many lives, human error remains
a significant challenge, as missing a polyp could have fatal consequences for
the patient. Deep learning (DL) polyp detectors offer a promising solution.
However, existing DL polyp detectors often mistake white light reflections from
the endoscope for polyps, which can lead to false positives.To address this
challenge, in this paper, we propose a novel data augmentation approach that
artificially adds more white light reflections to create harder training
scenarios. Specifically, we first generate a bank of artificial lights using
the training dataset. Then we find the regions of the training images that we
should not add these artificial lights on. Finally, we propose a sliding window
method to add the artificial light to the areas that fit of the training
images, resulting in augmented images. By providing the model with more
opportunities to make mistakes, we hypothesize that it will also have more
chances to learn from those mistakes, ultimately improving its performance in
polyp detection. Experimental results demonstrate the effectiveness of our new
data augmentation method.
