# Tool and Phase recognition using contextual CNN features

Source: [Tool and Phase recognition using contextual CNN features](<https://www.sota2.com/research/paper/68fa62d91e209131ce9fba73>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:28:18.594Z

Authors: Manish Sahu, Anirban Mukhopadhyay, Angelika Szengel, Stefan Zachow

Published: 2016-10-27T15:54:41.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.08854>)

[PDF](<https://arxiv.org/pdf/1610.08854.pdf>)