Few-Shot Learning for Postnatal Gestational Age Estimation

A baby's gestational age determines whether or not they are premature, which helps clinicians decide treatment. The most accurate dating methods use Ultrasound Scans, but these are expensive, require trained personnel and cannot always be deployed to remote areas. In the absence of such methods, the Ballard Score, a postnatal clinical examination, can be used. However, this method is highly subjective and results vary widely depending on the experience of the examiner. In the last decade, there have been efforts to exploit machine learning methods to create reliable postnatal methods deployable anywhere in the world. However, current state of the art methods remain influenced by the quality of their input data, which is a major issue in areas where data collection is difficult or impossible. Gathering images of newborns, especially those who are premature, is a very challenging task, due to the intrusiveness of taking photographs inside an ICU. This paper explores few-shot learning for gestational age estimation and investigates whether it can be used as an alternative way to explicitly deal with a lack of data. We compare two popular methods for few-shot learning, Model Agnostic Meta-Learning and Prototypical Networks, with a small dataset collected as part of the GesATional Project. Experimental results show that few-shot learning can be used to estimate gestational age postnatally. As a novel contribution to deal with the lack of data, models are trained with CelebFaces Attributes, to aid the procedure. We demonstrate that the novel integration of additional meta-sets improves performance by an average of 2.3% for both models comparing to just using a single dataset.