Learning Subjective Image Quality Assessment for Transvaginal Ultrasound Scans from Multi-Annotator Labels

Daniel Petashvili, Hu Wang, Alison Deslandes, Jodie Avery, George Condous, Gustavo Carneiro, Louise Hull, Hsiang-Ting Chen

Abstract: This paper proposes a novel AI model that automatically assesses the quality of transvaginal ultrasound (TVUS) images, offering support to sonographers, especially those still learning, in acquiring high-quality scans for gynecological pathology diagnosis. Addressing the challenge of varying interpretations by different medical professionals, this model approaches the issue as a multi-annotator noisy label problem. Our novel machine learning architecture first aggregates quality assessments from multiple raters using a weighted ensemble algorithm to estimate consensus labels. The model then employs a multi-axis vision transformer to enhance the process of image quality evaluation. We evaluated the model on a new multi-annotator TVUS dataset, where our model successfully predicted image quality with an accuracy of 80%. This development represents an exciting first step in empowering sonographers to assess scan quality on the spot, reduce the need for repeated imaging, and improve the diagnosis of gynecological pathology.