OCLNet: Obfuscation feature Contrastive Learning Network for Weakly Supervised Semantic Segmentation on Ultrasound Images

Abstract

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current research.Among various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis.