DaFoEs: Mixing Datasets towards the generalization of vision-state
  deep-learning Force Estimation in Minimally Invasive Robotic Surgery
Mikel De Iturrate Reyzabal Mingcong Chen Wei Huang Sebastien Ourselin Hongbin Liu
Precisely determining the contact force during safe interaction in Minimally
Invasive Robotic Surgery (MIRS) is still an open research challenge. Inspired
by post-operative qualitative analysis from surgical videos, the use of
cross-modality data driven deep neural network models has been one of the
newest approaches to predict sensorless force trends. However, these methods
required for large and variable datasets which are not currently available. In
this paper, we present a new vision-haptic dataset (DaFoEs) with variable soft
environments for the training of deep neural models. In order to reduce the
bias from a single dataset, we present a pipeline to generalize different
vision and state data inputs for mixed dataset training, using a previously
validated dataset with different setup. Finally, we present a variable
encoder-decoder architecture to predict the forces done by the laparoscopic
tool using single input or sequence of inputs. For input sequence, we use a
recurrent decoder, named with the prefix R, and a new temporal sampling to
represent the acceleration of the tool. During our training, we demonstrate
that single dataset training tends to overfit to the training data domain, but
has difficulties on translating the results across new domains. However,
dataset mixing presents a good translation with a mean relative estimated force
error of 5% and 12% for the recurrent and non-recurrent models respectively.
Our method, also marginally increase the effectiveness of transformers for
force estimation up to a maximum of ~15%, as the volume of available data is
increase by 150%. In conclusion, we demonstrate that mixing experimental set
ups for vision-state force estimation in MIRS is a possible approach towards
the general solution of the problem.