NEURAL ARCHITECTURE SEARCH FOR FRACTURE CLASSIFICATION

The adoption by radiologists of deep-learning based solutions to the bone fracture problem has helped improved diagnostic performances and patient care. The base models behind these tools were initially designed to solve problems on natural images, favoring transfer learning between standard image datasets and sets of radiographs. Those architectures could yet be made more specific to radiographs using neural architecture search (NAS). Unfortunately, current NAS approaches do not benefit from transfer learning. In this paper, we introduce an efficient scheme to exploit transfer learning when performing NAS. Using our approach, we validate the architecture tailoring paradigm to radiographs. On a custom fracture classification task, we find a new model with improved performances and reduced computational overhead over its counterparts pre-trained on ImageNet.