# Active Learning for Efficient Annotation of Surgical Videos with Weak Supervision

arXiv: https://arxiv.org/abs/2607.13237 · HTML: https://mlsanity.com/paper/2607.13237/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models](https://mlsanity.com/paper/2412.07679/) (2024)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [Active Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2303.08348/) (2023)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video](https://mlsanity.com/paper/2007.02501/) (2020)
- [Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation](https://mlsanity.com/paper/2203.15177/) (2022)
- [Active Learning Strategies for Weakly-supervised Object Detection](https://mlsanity.com/paper/2207.12112/) (2022)
- [ALWOD: Active Learning for Weakly-Supervised Object Detection](https://mlsanity.com/paper/2309.07914/) (2023)
- [Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video](https://mlsanity.com/paper/2407.15794/) (2024)
- [Slot-BERT: Self-supervised Object Discovery in Surgical Video](https://mlsanity.com/paper/2501.12477/) (2025)
- [ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery](https://mlsanity.com/paper/2511.16322/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort](https://mlsanity.com/paper/2610.20396/) (2026)

## Abstract (arXiv)

Precise spatial-temporal annotation of laparoscopic videos is time-consuming and requires expert knowledge. We propose a human-in-the-loop knowledge acquisition framework that combines active learning with dual-loss optimization to significantly reduce the annotation effort needed for automatic localization and segmentation of objects in the surgical field. Our method employs a foundation model to generate temporally consistent class activation maps (CAMs) from video using two complementary training objectives: a weak supervision loss on video-level tool presence labels for weakly annotated data, and an image-level mask loss on human-corrected annotations obtained through active learning. Rather than requiring dense pixel-level annotation upfront, our pipeline iteratively proposes pseudo-masks that guide the expert annotator to refine the knowledge previously captured by the model. We demonstrate that our framework reduces the effort of surgical video annotation by 50% by the end of training in comparison to fully manual annotation. Through eliminating the need for large, fully annotated datasets from the start, this framework enables scalability to the development of surgical tool segmentation models. This iterative human-in-the-loop refinement supports efficient knowledge acquisition with minimal expert input, providing a practical and deployable strategy for expanding tool segmentation to larger, more diverse datasets and real-world clinical settings.