Curriculum Learning for Few-Shot Domain Adaptation in CT-Based Airway Tree Segmentation

Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts. We address this by integrating Curriculum Learning (CL) into segmentation networks, using ad-hoc complexity scores derived from CT scans and ground-truth tree features. We specifically investigate few-shot domain adaptation, targeting scenarios where manual annotation of a full fine-tuning dataset is prohibitively expensive. Results are reported on two large open-cohorts (ATM22 and AIIB23) with high performance using CL for full training (Source domain) and fewshot fine-tuning (Target domain), but also some insights on potential detrimental effects when using a classic Bootstrapping scoring function or indadequate scan sequencing.