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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

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Surgical Tool Classification in Laparoscopic Videos Using Convolutional Neural Network

Tamer Abdulbaki Alshirbaji / Nour Aldeen Jalal / Knut Möller
Published Online: 2018-09-22 | DOI: https://doi.org/10.1515/cdbme-2018-0097


Laparoscopic videos are a very important source of information which is inherently available in minimally invasive surgeries. Detecting surgical tools based on that videos have gained increasing interest due to its importance in developing a context-aware system. Such system can provide guidance assistance to the surgical team and optimise the processes inside the operating room. Convolutional neural network is a robust method to learn discriminative visual features and classify objects. As it expects a uniform distribution of data over classes, it fails to identify classes which are under-presented in the training data. In this work, loss-sensitive learning approach and resampling techniques were applied to counter the negative effects of imbalanced laparoscopic data on training the CNN model. The obtained results showed improvement in the classification performance especially for detecting surgical tools which are shortly used in the procedure.

Keywords: Surgical tool detection; loss-sensitive learning; laparoscopic videos; imbalanced data

About the article

Published Online: 2018-09-22

Published in Print: 2018-09-01

Citation Information: Current Directions in Biomedical Engineering, Volume 4, Issue 1, Pages 407–410, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2018-0097.

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