Show simple item record

dc.contributor.authorMarbán González, Arturo
dc.contributor.authorSrinivasan, Vignesh
dc.contributor.authorSamek, Wojciech
dc.contributor.authorFernández Ruzafa, José
dc.contributor.authorCasals Gelpi, Alicia
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-05-03T11:40:32Z
dc.date.available2019-05-03T11:40:32Z
dc.date.issued2018
dc.identifier.citationMarbán, A. [et al.]. Estimation of interaction forces in robotic surgery using a semi-supervised deep neural network model. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Towards a robotic society: October, 1-5, 2018, Madrid, Spain, Madrid Municipal Conference Centre". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 761-768.
dc.identifier.isbn978-1-5386-8094-0
dc.identifier.urihttp://hdl.handle.net/2117/132610
dc.description.abstractProviding force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems still remains a challenge. In recent years, Vision-Based Force Sensing (VBFS) has emerged as a promising approach to address this problem. Existing methods have been developed in a Supervised Learning (SL) setting. Nonetheless, most of the video sequences related to robotic surgery are not provided with ground-truth force data, which can be easily acquired in a controlled environment. A powerful approach to process unlabeled video sequences and find a compact representation for each video frame relies on using an Unsupervised Learning (UL) method. Afterward, a model trained in an SL setting can take advantage of the available ground-truth force data. In the present work, UL and SL techniques are used to investigate a model in a Semi-Supervised Learning (SSL) framework, consisting of an encoder network and a Long-Short Term Memory (LSTM) network. First, a Convolutional Auto-Encoder (CAE) is trained to learn a compact representation for each RGB frame in a video sequence. To facilitate the reconstruction of high and low frequencies found in images, this CAE is optimized using an adversarial framework and a L1-loss, respectively. Thereafter, the encoder network of the CAE is serially connected with an LSTM network and trained jointly to minimize the difference between ground-truth and estimated force data. Datasets addressing the force estimation task are scarce. Therefore, the experiments have been validated in a custom dataset. The results suggest that the proposed approach is promising.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshRobotics in medicine
dc.subject.lcshRobot vision
dc.subject.lcshMachine learning
dc.subject.otherDeep neural networks
dc.subject.otherRobotic surgery
dc.subject.otherSemi-supervised learning
dc.subject.otherVision based force sensing
dc.titleEstimation of interaction forces in robotic surgery using a semi-supervised deep neural network model
dc.typeConference report
dc.subject.lemacRobòtica en medicina
dc.subject.lemacVisió artificial (Robòtica)
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes
dc.identifier.doi10.1109/IROS.2018.8593701
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8593701
dc.rights.accessOpen Access
drac.iddocument24026543
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/779813/EU/Smart Autonomous Robotic Assistant Surgeon/SARAS
upcommons.citation.authorMarbán, A.; Srinivasan, V.; Samek, W.; Fernández, J.; Casals, A.
upcommons.citation.contributorIEEE/RSJ International Conference on Intelligent Robots and Systems
upcommons.citation.publishedtrue
upcommons.citation.publicationName2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Towards a robotic society: October, 1-5, 2018, Madrid, Spain, Madrid Municipal Conference Centre
upcommons.citation.startingPage761
upcommons.citation.endingPage768


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder