  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. We release
the code, checkpoints, and high-quality labels of the CVA-BUS dataset in
https://github.com/HaojunYu1998/UltraDet.
