EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification

• URL: http://arxiv.org/abs/2510.17200v1
• Date: Mon, 20 Oct 2025 06:26:54 GMT
• Title: EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification
• Authors: Bingrong Liu, Jun Shi, Yushan Zheng,
• Abstract summary: Class-incremental learning (CIL) for endoscopic image analysis is crucial for real-world clinical applications.<n>We propose EndoCIL, a novel and unified CIL framework specifically tailored for endoscopic image diagnosis.
• Score: 5.574295682041076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-incremental learning (CIL) for endoscopic image analysis is crucial for real-world clinical applications, where diagnostic models should continuously adapt to evolving clinical data while retaining performance on previously learned ones. However, existing replay-based CIL methods fail to effectively mitigate catastrophic forgetting due to severe domain discrepancies and class imbalance inherent in endoscopic imaging. To tackle these challenges, we propose EndoCIL, a novel and unified CIL framework specifically tailored for endoscopic image diagnosis. EndoCIL incorporates three key components: Maximum Mean Discrepancy Based Replay (MDBR), employing a distribution-aligned greedy strategy to select diverse and representative exemplars, Prior Regularized Class Balanced Loss (PRCBL), designed to alleviate both inter-phase and intra-phase class imbalance by integrating prior class distributions and balance weights into the loss function, and Calibration of Fully-Connected Gradients (CFG), which adjusts the classifier gradients to mitigate bias toward new classes. Extensive experiments conducted on four public endoscopic datasets demonstrate that EndoCIL generally outperforms state-of-the-art CIL methods across varying buffer sizes and evaluation metrics. The proposed framework effectively balances stability and plasticity in lifelong endoscopic diagnosis, showing promising potential for clinical scalability and deployment.

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