# Tool and Phase recognition using contextual CNN features

arXiv: https://arxiv.org/abs/1610.08854 · HTML: https://mlsanity.com/paper/1610.08854/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge](https://mlsanity.com/paper/2102.13644/) (2021)
- [A semi-supervised Teacher-Student framework for surgical tool detection and localization](https://mlsanity.com/paper/2208.09926/) (2022)
- [A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis](https://mlsanity.com/paper/2103.16847/) (2021)
- [Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks](https://mlsanity.com/paper/2109.15063/) (2021)
- [Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data](https://mlsanity.com/paper/2307.01232/) (2023)
- [Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning](https://mlsanity.com/paper/2510.15372/) (2025)

## Abstract (arXiv)

A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, methods are developed for generating contextual features and combining them with time series analysis for final classification using multi-class random forest. The proposed pipeline is tested over the training and testing datasets of M2CAI16 challenges: tool and phase detection. Encouraging results are obtained by leave-one-out cross validation evaluation on the training dataset.