3 research outputs found
Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives
This article reviews the recent advances in intelligent robotic ultrasound
(US) imaging systems. We commence by presenting the commonly employed robotic
mechanisms and control techniques in robotic US imaging, along with their
clinical applications. Subsequently, we focus on the deployment of machine
learning techniques in the development of robotic sonographers, emphasizing
crucial developments aimed at enhancing the intelligence of these systems. The
methods for achieving autonomous action reasoning are categorized into two sets
of approaches: those relying on implicit environmental data interpretation and
those using explicit interpretation. Throughout this exploration, we also
discuss practical challenges, including those related to the scarcity of
medical data, the need for a deeper understanding of the physical aspects
involved, and effective data representation approaches. Moreover, we conclude
by highlighting the open problems in the field and analyzing different possible
perspectives on how the community could move forward in this research area.Comment: Accepted by Annual Review of Control, Robotics, and Autonomous
System
Representation disentanglement for multi-task learning with application to fetal ultrasound
One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are maximally informative with respect to relevant tasks, while an adversarial regularization encourages these features to be disentangled and minimally informative about irrelevant tasks. We aim to use the disentangled representations to generalize the applicability of deep neural networks. We demonstrate the advantages of the proposed method on synthetic data as well as fetal ultrasound images. Our experiments illustrate that our method is capable of learning disentangled internal representations. It outperforms baseline methods in multiple tasks, especially on images with new properties, e.g. previously unseen artifacts in fetal ultrasound