From Noise to Clarity: Generating Realistic Fetal Ultrasound Images with Diffusion Models
Abstract
Ultrasound imaging is a critical tool in maternal-fetal healthcare, aiding in fetal development assessment and anomaly detection. However, the effectiveness of deep learning mod els in fetal ultrasound analysis is often hindered by the scarcity of highquality datasets. The challenges of patient variability, operator dependency, and ethical constraints further exacerbate this issue, limiting the development of robust AI-based diagnostic tools. Existing generative models attempt to bridge this gap, but they frequently require manual intervention and finetuning to ensure anatomical accuracy and clinical relevance. While some recent approaches aim to automate the generative process, such as self-supervised learning and active learning techniques, they often struggle with generating anatomically diverse images or maintaining clinical fidelity across gestational stages. These limitations highlight the need for a more adaptive and autonomous framework capable of continu ously improving its outputs without human oversight. To address these challenges, this work presents a novel generative framework integrating diffusion-based synthesis and self supervised learning to enhance fetal ultrasound image generation. A key component of this work is DiFUSAL: Diffusion Based Fetal Ultrasound Synthesis with Active Learning, a self-improving diffusion model that iteratively refines its output through an active learning paradigm. Unlike prior models, DiFUSAL autonomously evaluates and enhances…
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