Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be approximately domain-invariant. However, interpretable models typically underperform compared to their Blackbox (BB) variants. We start with a BB in the source domain and distill it into a \emph{mixture} of shallow interpretable models using human-understandable concepts. As each interpretable model covers a subset of data, a mixture of interpretable models achieves comparable performance as BB. Further, we use the pseudo-labeling technique from semi-supervised learning (SSL) to learn the concept classifier in the target domain, followed by fine-tuning the interpretable models in the target domain. We evaluate our model using a real-life large-scale chest-X-ray (CXR) classification dataset. The code is available at: \url{https://github.com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs}.

翻译：构建通用型AI模型是医疗健康领域的主要挑战之一。放射科医生依赖通用性的异常描述规则，而神经网络（NN）模型即使在输入分布发生微小变化（如扫描仪类型）时也会性能下降。通过微调模型将知识从一个领域迁移到另一个领域，需要在目标领域拥有大量标注数据。本文开发了一种可解释模型，能够以最小计算成本高效微调至未见过的目标领域。我们假设NN的可解释组件具有近似领域不变性。然而，可解释模型的性能通常逊于其黑盒（BB）变体。我们从源领域的BB模型出发，利用人类可理解的概念将其蒸馏为浅层可解释模型的混合体。由于每个可解释模型覆盖部分数据，可解释模型混合体的性能可与BB模型相当。此外，我们采用半监督学习（SSL）中的伪标签技术学习目标领域的概念分类器，随后在目标领域中微调可解释模型。我们利用真实大规模胸部X光（CXR）分类数据集对模型进行评估。代码参见：\url{https://github.com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs}。