Cross-Dataset Adaptation for Instrument Classification in Cataract
  Surgery Videos
Jay N. Paranjape Shameema Sikder Vishal M. Patel S. Swaroop Vedula
Surgical tool presence detection is an important part of the intra-operative
and post-operative analysis of a surgery. State-of-the-art models, which
perform this task well on a particular dataset, however, perform poorly when
tested on another dataset. This occurs due to a significant domain shift
between the datasets resulting from the use of different tools, sensors, data
resolution etc. In this paper, we highlight this domain shift in the commonly
performed cataract surgery and propose a novel end-to-end Unsupervised Domain
Adaptation (UDA) method called the Barlow Adaptor that addresses the problem of
distribution shift without requiring any labels from another domain. In
addition, we introduce a novel loss called the Barlow Feature Alignment Loss
(BFAL) which aligns features across different domains while reducing redundancy
and the need for higher batch sizes, thus improving cross-dataset performance.
The use of BFAL is a novel approach to address the challenge of domain shift in
cataract surgery data. Extensive experiments are conducted on two cataract
surgery datasets and it is shown that the proposed method outperforms the
state-of-the-art UDA methods by 6%. The code can be found at
https://github.com/JayParanjape/Barlow-Adaptor