Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is difficult and expensive since it requires expert annotators like doctors, radiologists, etc. Active learning (AL) is a well-known method to mitigate labeling costs by selecting the most diverse or uncertain samples. However, current AL methods do not work well in the medical imaging domain with OOD data. We propose Diagnose (avoiDing out-of-dIstribution dAta usinG submodular iNfOrmation meaSurEs), a novel active learning framework that can jointly model similarity and dissimilarity, which is crucial in mining in-distribution data and avoiding OOD data at the same time. Particularly, we use a small number of data points as exemplars that represent a query set of in-distribution data points and a private set of OOD data points. We illustrate the generalizability of our framework by evaluating it on a wide variety of real-world OOD scenarios. Our experiments verify the superiority of Diagnose over the state-of-the-art AL methods across multiple domains of medical imaging.
翻译:避免分配(OOD)数据对于在医疗成像领域培训受监督的机器学习模式至关重要。此外,获得贴标签的医疗数据十分困难和昂贵,因为它需要医生、放射学家等专家顾问等专家顾问。积极学习(AL)是通过选择最多样化或最不确定的样本来降低标签成本的众所周知的方法。然而,目前AL方法在医疗成像领域与OOD数据不起作用。我们提议Diagnose(对Data usinG 亚模化 iNfOrmation meaSurEs(Ata usin G 亚模化 iNfODASurEs),这是一个新的积极学习框架,可以共同模拟相似性和差异性,这对于开采分布数据并同时避免OOD数据至关重要。我们使用少量的数据点作为Explators,代表着一组分配数据点和一套私人ODD数据点的查询。我们通过评估各种现实世界OD情景来说明我们的框架的普遍适用性。我们的实验核查了DAqnose多重图像域的优势。