Abstract
Ovarian cancer remains one of the most lethal gynecological malignancies, posing significant challenges for early detection due to its asymptomatic nature in early stages. Accurate segmentation of ovarian tumors from ultrasound images is critical for improving diagnostic accuracy and patient outcomes. In this study, we introduce SMoFFI-SegFormer, an advanced deep learning model specifically designed to enhance multi-scale feature representation and address the complexities of ovarian tumor segmentation. Building upon the SegFormer architecture, SMoFFI-SegFormer incorporates a novel Self-modulate Fusion with Feature Inhibition (SMoFFI) module that promotes cross-scale information exchange and effectively handles spatial heterogeneity within tumors. Through extensive experimentation on two public datasets-OTU_2D and OTU_CEUS-our model demonstrates superior performance with high overall accuracy, mean Intersection over Union (mIoU), and class accuracy. Specifically, SMoFFI-SegFormer achieves state-of-the-art results, significantly outperforming existing models in both segmentation precision and efficiency. This work paves the way for more reliable and automated tools in the diagnosis and management of ovarian cancer.