  To address overfitting and enhance model generalization in
gastroenterological polyp size assessment, our study introduces
Feature-Selection Gates (FSG) or Hard-Attention Gates (HAG) alongside Gradient
Routing (GR) for dynamic feature selection. This technique aims to boost
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) by
promoting sparse connectivity, thereby reducing overfitting and enhancing
generalization. HAG achieves this through sparsification with learnable
weights, serving as a regularization strategy. GR further refines this process
by optimizing HAG parameters via dual forward passes, independently from the
main model, to improve feature re-weighting. Our evaluation spanned multiple
datasets, including CIFAR-100 for a broad impact assessment and specialized
endoscopic datasets (REAL-Colon, Misawa, and SUN) focusing on polyp size
estimation, covering over 200 polyps in more than 370,000 frames. The findings
indicate that our HAG-enhanced networks substantially enhance performance in
both binary and triclass classification tasks related to polyp sizing.
Specifically, CNNs experienced an F1 Score improvement to 87.8% in binary
classification, while in triclass classification, the ViT-T model reached an F1
Score of 76.5%, outperforming traditional CNNs and ViT-T models. To facilitate
further research, we are releasing our codebase, which includes implementations
for CNNs, multistream CNNs, ViT, and HAG-augmented variants. This resource aims
to standardize the use of endoscopic datasets, providing public
training-validation-testing splits for reliable and comparable research in
gastroenterological polyp size estimation. The codebase is available at
github.com/cosmoimd/feature-selection-gates.
