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.

提出了一种名为 UltraDet 的模型，通过集成负上下文（NTC）以抑制假阳性，结合反向光流引导提取前一帧的上下文信息，在超声扫描过程中能够帮助医生更准确地诊断癌症。