Knowledge Distillation Approach for SOS Fusion Staging: Towards Fully Automated Skeletal Maturity Assessment
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
We introduce a novel deep learning framework for the automated staging of
spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker in
both orthodontics and forensic anthropology. Our approach leverages a
dual-model architecture wherein a teacher model, trained on manually cropped
images, transfers its precise spatial understanding to a student model that
operates on full, uncropped images. This knowledge distillation is facilitated
by a newly formulated loss function that aligns spatial logits as well as
incorporates gradient-based attention spatial mapping, ensuring that the
student model internalizes the anatomically relevant features without relying
on external cropping or YOLO-based segmentation. By leveraging expert-curated
data and feedback at each step, our framework attains robust diagnostic
accuracy, culminating in a clinically viable end-to-end pipeline. This
streamlined approach obviates the need for additional pre-processing tools and
accelerates deployment, thereby enhancing both the efficiency and consistency
of skeletal maturation assessment in diverse clinical settings.
