We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable. If a predictive machine is made invariant to a set of domains, the accuracy of the output predictions (as measured by mutual information) is limited by the domain with the least amount of information to begin with. If a real label value is highly informative about the source domain, it cannot be accurately predicted by an invariant predictor. These results are simple and intuitive, but we believe that it is beneficial to state them for medical imaging harmonization.
翻译:我们发现,对于一系列广泛的统一/常备性计划来说,若干不可取的特性是不可避免的。 如果对一组域进行预测,那么(根据相互信息衡量的)输出预测的准确性会受到最小信息量的域的限制。如果一个真实的标签值对源域的信息量很高,则无法由一个不固定的预测家准确预测。这些结果既简单又直观,但我们认为,为了医学成像的统一,说明这些结果是有益的。