Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic forgetting issue, a novel adapter-based strategy is proposed to help effectively learn a set of new diseases at each round (or task) of continual learning, without changing the shared feature extractor. The learnable lightweight task-specific adapter(s) can be flexibly designed (e.g., two convolutional layers) and then added to the pretrained and fixed feature extractor. Together with a specially designed task-specific head which absorbs all previously learned old diseases as a single 'out-of-distribution' category, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are comparable and consequently the appropriate classifier head can be more accurately selected during model inference. Extensive empirical evaluations on three image datasets demonstrate the superior performance of the proposed method in continual learning of new diseases. The source code will be released publicly.

翻译：目前，智能诊断系统在部署后缺乏持续学习诊断新疾病的能力，同时需保留旧疾病知识。特别是，使用新疾病的训练数据更新智能诊断系统会导致旧疾病知识的灾难性遗忘。为了解决这一灾难性遗忘问题，提出了一种新颖的基于适配器的策略，以在不改变共享特征提取器的情况下，帮助在持续学习的每轮（或每项任务）中有效学习一组新疾病。可学习的轻量级任务特定适配器可以灵活设计（例如，两个卷积层），然后添加到预训练且固定的特征提取器中。结合一个专门设计的任务特定头部（该头部将所有先前学习过的旧疾病吸收为单一的‘分布外’类别），任务特定适配器可以帮助预训练特征提取器更有效地提取疾病间的判别特征。此外，采用一种简单而有效的微调方法，对多个任务特定头部进行协同微调，使不同头部的输出具有可比性，从而在模型推理过程中能够更准确地选择相应的分类器头部。在三个图像数据集上的广泛实证评估表明，所提方法在持续学习新疾病方面具有优越性能。源代码将公开。