Background: We present an artificial intelligence framework for vascularity classification of the gallbladder (GB) wall from intraoperative images of laparoscopic cholecystectomy (LC).
Methods: A two-stage Multiple Instance Convolutional Neural Network is proposed. First, a convolutional autoencoder is trained to extract feature representations from 4585 patches of GB images. The second model includes a multi-instance encoder that fetches random patches from a GB region and outputs an equal number of embeddings that feed a multi-input classification module, which employs pooling and self-attention mechanisms, to perform prediction.
Results: The evaluation was performed on 234 GB images of low and high vascularity from 68 LC videos. Thorough comparison with various state-of-the-art multi-instance and single-instance learning algorithms was performed for two experimental tasks: image- and video-level classification. The proposed framework shows the best performance with accuracy 92.6%-93.2% and F1 93.5%-93.9%, close to the agreement of two expert evaluators (94%).
Conclusions: The proposed technique provides a novel approach to classify LC operations with respect to the vascular pattern of the GB wall.
Keywords: CNN; artificial intelligence; deep learning; gallbladder; laparoscopic cholecystectomy; multiple instance.
© 2022 John Wiley & Sons Ltd.