Imperial College London
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Dr Dandan Zhang

Faculty of Engineering, Department of Bioengineering

Lecturer in Artificial Intelligence & Machine Learning
 
 
 
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Contact

 

d.zhang17 Website

 
 
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Location

 

402Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Zhang:2021:10.1109/ICRA48506.2021.9561803,
author = {Zhang, D and Wang, R and Lo, B},
doi = {10.1109/ICRA48506.2021.9561803},
pages = {1350--1356},
publisher = {IEEE},
title = {Surgical gesture recognition based on bidirectional multi-layer independently RNN with explainable spatial feature extraction},
url = {http://dx.doi.org/10.1109/ICRA48506.2021.9561803},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Minimally  invasive  surgery  mainly  consists  of  a series of sub-tasks, which can be decomposed into basic gestures or contexts. As a prerequisite of autonomic operation, surgical gesture  recognition  can  assist  motion  planning  and  decision-making,  and  build  up  context-aware  knowledge  to  improve the  surgical  robot  control  quality.  In  this  work,  we  aim  to develop an effective surgical gesture recognition approach with an  explainable  feature  extraction  process. A   Bidirectional   Multi-Layer   independently   RNN   (BML-indRNN) model is proposed in this paper, while spatial feature extraction  is  implemented  via  fine-tuning  of  a  Deep  Convolutional Neural Network (DCNN) model constructed based on the VGG architecture. To eliminate the black-box effects of DCNN, Gradient-weighted  Class  Activation  Mapping  (Grad-CAM)  is employed.  It  can  provide  explainable  results  by  showing  the regions  of  the  surgical  images  that  have  a  strong  relationship with  the  surgical  gesture  classification  results. The  proposed  method  was  evaluated  based  on  the  suturing task  with  data  obtained  from  the  public  available  JIGSAWS database. Comparative studies were conducted to verify the pro-posed  framework.  Results  indicated  that  the  testing  accuracy  for the suturing task based on our proposed method is 87.13%,which  outperforms  most  of  the  state-of-the-art  algorithms
AU  - Zhang,D
AU  - Wang,R
AU  - Lo,B
DO  - 10.1109/ICRA48506.2021.9561803
EP  - 1356
PB  - IEEE
PY  - 2021///
SP  - 1350
TI  - Surgical gesture recognition based on bidirectional multi-layer independently RNN with explainable spatial feature extraction
UR  - http://dx.doi.org/10.1109/ICRA48506.2021.9561803
UR  - http://hdl.handle.net/10044/1/88436
ER  -
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