During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC). To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. To facilitate future research, we will release the code, checkpoints, and high-quality labels of the CVA-BUS dataset used in our experiments.

翻译：在超声扫描过程中，实时病灶检测可辅助放射科医生进行准确癌症诊断。然而，这一关键任务仍具挑战性且研究不足。通用实时目标检测模型应用于超声视频时可能错误报告明显假阳性，可能误导初级放射科医生。其中一个关键问题是它们未能利用先前帧中的阴性症状（称为负时间上下文）。为解决此问题，我们提出在逆光流引导下从先前帧中提取包含负时间上下文在内的上下文信息。通过聚合所提取的上下文，模型得以利用负时间上下文抑制假阳性。我们将此模型命名为UltraDet。所提出的UltraDet相较于此前最优方法取得显著提升，并实现实时推理速度。为促进未来研究，我们将公开实验所用CVA-BUS数据集的代码、模型权重及高质量标注。