Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection
This addresses false positives in ultrasound lesion detection for radiologists, particularly junior ones, by leveraging temporal information, representing an incremental advance in domain-specific detection.
The paper tackled the problem of false positives in real-time ultrasound lesion detection by proposing UltraDet, a model that uses negative temporal contexts from previous frames to suppress false positives, achieving significant improvement over previous state-of-the-art methods with real-time inference speed.
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.