527 research outputs found
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Over the past few years, surgical data science has attracted substantial
interest from the machine learning (ML) community. Various studies have
demonstrated the efficacy of emerging ML techniques in analysing surgical data,
particularly recordings of procedures, for digitizing clinical and non-clinical
functions like preoperative planning, context-aware decision-making, and
operating skill assessment. However, this field is still in its infancy and
lacks representative, well-annotated datasets for training robust models in
intermediate ML tasks. Also, existing datasets suffer from inaccurate labels,
hindering the development of reliable models. In this paper, we propose a
systematic methodology for developing robust models for surgical tool detection
using noisy data. Our methodology introduces two key innovations: (1) an
intelligent active learning strategy for minimal dataset identification and
label correction by human experts; and (2) an assembling strategy for a
student-teacher model-based self-training framework to achieve the robust
classification of 14 surgical tools in a semi-supervised fashion. Furthermore,
we employ weighted data loaders to handle difficult class labels and address
class imbalance issues. The proposed methodology achieves an average F1-score
of 85.88\% for the ensemble model-based self-training with class weights, and
80.88\% without class weights for noisy labels. Also, our proposed method
significantly outperforms existing approaches, which effectively demonstrates
its effectiveness
DeepPhase: Surgical Phase Recognition in CATARACTS Videos
Automated surgical workflow analysis and understanding can assist surgeons to
standardize procedures and enhance post-surgical assessment and indexing, as
well as, interventional monitoring. Computer-assisted interventional (CAI)
systems based on video can perform workflow estimation through surgical
instruments' recognition while linking them to an ontology of procedural
phases. In this work, we adopt a deep learning paradigm to detect surgical
instruments in cataract surgery videos which in turn feed a surgical phase
inference recurrent network that encodes temporal aspects of phase steps within
the phase classification. Our models present comparable to state-of-the-art
results for surgical tool detection and phase recognition with accuracies of 99
and 78% respectively.Comment: 8 pages, 3 figures, 1 table, MICCAI 201
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