On-the-Fly Point Annotation for Fast Medical Video Labeling
<|reference_start|>On-the-Fly Point Annotation for Fast Medical Video Labeling: Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. Methods: We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. Results: Using our method, annotation speed was 3.2x faster than the traditional annotation technique. We achieved a mean improvement of 6.51 +- 0.98 AP@50 over conventional method at equivalent annotation budgets on the developed dataset. Conclusion: Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.<|reference_end|>
@article{adrien2024on-the-fly,
    title={On-the-Fly Point Annotation for Fast Medical Video Labeling},
    author={Meyer Adrien and Mazellier Jean-Paul and Jeremy Dana and Nicolas Padoy},
    journal={arXiv preprint arXiv:2404.14344},
    year={2024},
    doi={10.1007/s11548-024-03098-y},
    archivePrefix={arXiv},
    eprint={2404.14344},
    primaryClass={cs.CV}
}