BiasPruner：面向医学图像分类的去偏持续学习
BiasPruner: Debiased Continual Learning for Medical Image Classification
AI总结：
针对医学图像分类的持续学习任务，提出BiasPruner框架，通过偏差分数剪去学习虚假特征的单元构建去偏子网络，新任务学习可复用旧网络单元，推理时用任务无关方法选最优子网络，在三个医学数据集上性能与公平性均优于SOTA方法。
摘要：
持续学习（CL）对实现网络的动态适配至关重要，能让网络在按顺序学习新任务时适配新数据和新类别，同时避免灾难性遗忘。与传统的持续学习视角不同，本文提出了一种新视角：遗忘实际上可能对序列学习范式有益。具体而言，我们提出了BiasPruner这一持续学习框架，它会主动遗忘训练数据中可能导致捷径学习的虚假相关性。该框架利用一种新的偏差分数来衡量网络中每个单元对学习虚假特征的贡献，剪去偏差分数最高的单元，形成为特定任务保留的去偏子网络。当BiasPruner学习新任务时，会构建新的去偏子网络，且可能纳入先前子网络中的单元，这提升了对新任务的适配能力和性能。推理阶段，BiasPruner采用简单的任务无关方法，选择最优的去偏子网络进行预测。我们在三个用于皮肤病变分类和胸部X光分类的医学数据集上开展实验，结果表明BiasPruner在分类性能和公平性方面始终优于当前最优（SOTA）的持续学习方法。我们的代码可在此处获取。
英文摘要：
Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetting. Diverging from conventional perspectives on CL, our paper introduces a new perspective wherein forgetting could actually benefit the sequential learning paradigm. Specifically, we present BiasPruner, a CL framework that intentionally forgets spurious correlations in the training data that could lead to shortcut learning. Utilizing a new bias score that measures the contribution of each unit in the network to learning spurious features, BiasPruner prunes those units with the highest bias scores to form a debiased subnetwork preserved for a given task. As BiasPruner learns a new task, it constructs a new debiased subnetwork, potentially incorporating units from previous subnetworks, which improves adaptation and performance on the new task. During inference, BiasPruner employs a simple task-agnostic approach to select the best debiased subnetwork for predictions. We conduct experiments on three medical datasets for skin lesion classification and chest X-Ray classification and demonstrate that BiasPruner consistently outperforms SOTA CL methods in terms of classification performance and fairness. Our code is available here.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
