RECORD
On-the-Fly Point Annotation for Fast Medical Video Labeling
Computer Vision and Pattern Recognition · Submitted 22 Apr 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Conclusion: Without bells and whistles, our approach offers a significant speed-up in annotation tasks.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s1 | This is particularly true for detection tasks where bounding box annotations are required. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s2 | The need to adjust two corners makes the process inherently frame-by-frame. | background 0.98 | 0.08 | 0.02 | asserted 1.00 |
| s3 | Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. | gap 0.53 | 0.04 | 0.02 | asserted 0.95 |
| s4 | Methods: We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. | contribution 0.81 | 0.85 | 0.03 | asserted 0.98 |
| s5 | 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. | method 0.99 | 0.62 | 0.04 | asserted 1.00 |
| s6 | This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. | method 0.93 | 0.23 | 0.03 | asserted 1.00 |
| s7 | We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. | method 0.93 | 0.96 | 0.02 | asserted 0.93 |
| s8 | Results: Using our method, annotation speed was 3.2x faster than the traditional annotation technique. | result 1.00 | 0.97 | 0.22 | asserted 1.00 |
| s9 | We achieved a mean improvement of 6.51 +- 0.98 AP@50 over conventional method at equivalent annotation budgets on the developed dataset. | result 1.00 | 0.97 | 0.10 | asserted 1.00 |
| s10 | Conclusion: Without bells and whistles, our approach offers a significant speed-up in annotation tasks. | interpretation 0.88 | 0.67 | 0.82 | asserted 1.00 |
| s11 | It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research. | interpretation 0.81 | 0.16 | 0.10 | asserted 0.74 |
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The paper
Original abstract
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.
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