Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-point regression. We evaluate the transferability of the learned representations to the task of ultrasound standard plane detection in two contexts. Firstly, we perform transfer learning by fine-tuning the CNN with a limited number of labeled standard plane images. We find that fine-tuning the saliency predictor is superior to training from random initialization, with an average F1-score improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters. We find that the attention models derive strong representations, approaching the precision of a fully-supervised baseline model for all but the last layer

Audio-visual modelling in a clinical setting

Auditory and visual signals are two primary perception modalities that are usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals—usually speech audio. In this study, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without relying on dense supervisory annotations from human experts for the model training. A simple yet effective multi-modal self-supervised learning framework is presented for this purpose. The proposed approach is able to help find standard anatomical planes, predict the focusing position of sonographer’s eyes, and localise anatomical regions of interest during ultrasound imaging. Experimental analysis on a large-scale clinical multi-modal ultrasound video dataset show that the proposed novel representation learning method provides good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions. Being able to learn such medical representations in a self-supervised manner will contribute to several aspects including a better understanding of obstetric imaging, training new sonographers, more effective assistive tools for human experts, and enhancement of the clinical workflow

Audio-visual modelling in a clinical setting

Auditory and visual signals are two primary perception modalities that are usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals—usually speech audio. In this study, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without relying on dense supervisory annotations from human experts for the model training. A simple yet effective multi-modal self-supervised learning framework is presented for this purpose. The proposed approach is able to help find standard anatomical planes, predict the focusing position of sonographer’s eyes, and localise anatomical regions of interest during ultrasound imaging. Experimental analysis on a large-scale clinical multi-modal ultrasound video dataset show that the proposed novel representation learning method provides good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions. Being able to learn such medical representations in a self-supervised manner will contribute to several aspects including a better understanding of obstetric imaging, training new sonographers, more effective assistive tools for human experts, and enhancement of the clinical workflow

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer's eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts