Deblurring masked image modeling for ultrasound image analysis

Qingbo Kang, Qicheng Lao, Jun Gao, Jingyan Liu, Huahui Yi, Buyun Ma, Xiaofan Zhang, Kang Li

Abstract: Highlights•We propose a specialized deblurring masked image modeling (MIM) approach tailored for ultrasound image analysis by incorporating a deblurring task into the pretraining proxy task.•We utilize a multi-scale hierarchical encoder architecture, enabling the extraction of both fine- and coarse-grained image representations.•We conduct pretraining experiments on 280,000 thyroid ultrasound images, and the downstream tasks encompass various disease diagnoses (nodule and Hashimoto’s thyroiditis), as well as different types of tasks (classification and segmentation).•As the first MIM-based work for ultrasound, we delve into fundamental considerations for modality-specific foundation models, yielding significant conclusions.