Spatiotemporal Uncertainty-Aware Mamba-Transformer Synergy: Breast Cancer Detection in ABUS

Breast cancer significantly affects women's health, making early and accurate diagnosis through ultrasound examination essential. However, automated breast ultrasound(ABUS) lesion detection models encounter challenges, including uncertain noise and difficulties in locating small lesions. This paper proposes SUA-MT, a multi-video object detection network using uncertainty-aware transformers and temporal-spatial mamba. To address the performance degradation caused by low-quality frames and noise interference in input data, we propose the uncertainty-gated transformer decoder (UGTD), which dynamically adjusts attention weights to focus on high-confidence regions while suppressing attention to redundant areas. The spatialtemporal(ST) mamba module is designed to model the long-term dependencies and 3D spatial features of different plane video frames, making full use of temporal and spatial information. In general, SUA-MT not only supports dynamic video length input, but also combines multidimensional video modeling of lesions (transverse, sagittal and coronal plane), making full use of the complementarity of multi-view information and spatiotemporal information to enhance the ability to locate small lesions. Experimental results on a combined set of internal and publicly datasets demonstrate that the SUA-MT method achieves state-of-the-art performance compared to existing video detection approaches. Specifically, SUA-MT attains a mean precision of 80.1 % and mean recall of 84.2 %, providing an efficient and robust solution for lesion detection in ABUS.