# Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos

arXiv: https://arxiv.org/abs/1806.05573 · HTML: https://mlsanity.com/paper/1806.05573/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 20 of 20)

- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery](https://mlsanity.com/paper/2312.11250/) (2023)
- [Image Compositing for Segmentation of Surgical Tools without Manual Annotations](https://mlsanity.com/paper/2102.09528/) (2021)
- [Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets](https://mlsanity.com/paper/2204.05235/) (2022)
- [CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection](https://mlsanity.com/paper/2302.06294/) (2023)
- [CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools](https://mlsanity.com/paper/2312.07352/) (2023)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [EgoSurgery-Tool: A Dataset of Surgical Tool and Hand Detection from Egocentric Open Surgery Videos](https://mlsanity.com/paper/2406.03095/) (2024)
- [Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network](https://mlsanity.com/paper/2406.08837/) (2024)
- [A semi-supervised Teacher-Student framework for surgical tool detection and localization](https://mlsanity.com/paper/2208.09926/) (2022)
- [Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery](https://mlsanity.com/paper/2402.01974/) (2024)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools](https://mlsanity.com/paper/2309.01723/) (2023)
- [On-the-Fly Point Annotation for Fast Medical Video Labeling](https://mlsanity.com/paper/2404.14344/) (2024)
- [Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos](https://mlsanity.com/paper/2401.02791/) (2024)
- [Efficient Surgical Tool Recognition via HMM-Stabilized Deep Learning](https://mlsanity.com/paper/2404.04992/) (2024)
- [DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging](https://mlsanity.com/paper/2601.13954/) (2026)
- [Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning](https://mlsanity.com/paper/2510.15372/) (2025)
- [AI for Quality Assurance in the Operating Room](https://mlsanity.com/paper/2606.30657/) (2026)

## Abstract (arXiv)

Surgical tool localization is an essential task for the automatic analysis of endoscopic videos. In the literature, existing methods for tool localization, tracking and segmentation require training data that is fully annotated, thereby limiting the size of the datasets that can be used and the generalization of the approaches. In this work, we propose to circumvent the lack of annotated data with weak supervision. We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos. Our architecture relies on a fully convolutional neural network, trained end-to-end, enabling us to localize surgical tools without explicit spatial annotations. We demonstrate the benefits of our approach on a large public dataset, Cholec80, which is fully annotated with binary tool presence information and of which 5 videos have been fully annotated with bounding boxes and tool centers for the evaluation.