Heterogeneity in medical imaging data is often tackled, in the context of machine learning, using domain invariance, i.e. deriving models that are robust to domain shifts, which can be both within domain (e.g. demographics) and across domains (e.g. scanner/protocol characteristics). However this approach can be detrimental to performance because it necessitates averaging across intra-class variability and reduces discriminatory power of learned models, in order to achieve better intra- and inter-domain generalization. This paper instead embraces the heterogeneity and treats it as a multi-task learning problem to explicitly adapt trained classifiers to both inter-site and intra-site heterogeneity. We demonstrate that the error of a base classifier on challenging 3D brain magnetic resonance imaging (MRI) datasets can be reduced by 2-3 times, in certain tasks, by adapting to the specific demographics of the patients, and different acquisition protocols. Learning the characteristics of domain shifts is achieved via auxiliary learning tasks leveraging commonly available data and variables, e.g. demographics. In our experiments, we use gender classification and age regression as auxiliary tasks helping the network weights trained on a source site adapt to data from a target site; we show that this approach improves classification accuracy by 5-30 % across different datasets on the main classification tasks, e.g. disease classification.

翻译：医疗成像数据中的异质性往往在机器学习的背景下被处理,使用领域差异,即产生对域变异具有活力的模型,这些模型可以是领域内(如人口统计)和跨领域(如扫描仪/方案特征)的模型,但这种方法可能不利于业绩,因为它需要在各类内部差异之间平均,并减少学习模型的歧视性力量,以便实现更好的内和部间一般化。本文包含异质性,并将其视为一个多任务学习问题,以明确使受过训练的分类师适应现场间和场内异质性。我们通过实验,我们利用经过训练的三维脑磁共振动成像(MRI)基本分类师的错误,在某些任务中可以减少2至3次,以适应病人的具体人口特征和不同的获取协议。通过利用现有的普通数据和变量,例如人口分类,学习域变的特征。我们在实验中,利用经过训练的现场分类,利用经过训练的分类的分类方法,将性别分类和不同年龄的回归数据作为辅助性数据源。我们用经过训练的网址改进了这个主要分类,从而改进了这个分类的网址,从而改进了这个分类。