Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark

Marina Ceccon, Davide Dalle Pezze, Alessandro Fabris, Gian Antonio Susto; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7163-7172

Despite the critical importance of the medical domain in Deep Learning most of the research in this area solely focuses on training models in static environments. It is only in recent years that research has begun to address dynamic environments and tackle the Catastrophic Forgetting problem through Continual Learning (CL) techniques. Previous studies have primarily focused on scenarios such as Domain Incremental Learning and Class Incremental Learning which do not fully capture the complexity of real-world applications. Therefore in this work we propose a novel benchmark combining the challenges of new class arrivals and domain shifts in a single framework by considering the New Instances and New Classes (NIC) scenario. This benchmark aims to model a realistic CL setting for the multi-label classification problem in medical imaging. Additionally it encompasses a greater number of tasks compared to previously tested scenarios. Specifically our benchmark consists of two datasets (NIH and CXP) nineteen classes and seven tasks a stream longer than the previously tested ones. To solve common challenges (e.g. the task inference problem) found in the CIL and NIC scenarios we propose a novel approach called Replay Consolidation with Label Propagation (RCLP). Our method surpasses existing approaches exhibiting superior performance with minimal forgetting.