Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi60581.2025.10980795

Authors: Maxime Jacovella, Ali Keshavarzi, Elsa Angelini

Institutions: Imperial College London, Télécom Paris, Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Grand Équipement National De Calcul Intensif