Convolutional-de-convolutional neural networks for recognition of surgical workflow

Convolutional-de-convolutional neural networks for recognition of surgical workflow

Blog Article

Computer-assisted surgery (CAS) has occupied an important position in modern surgery, further stimulating the progress of methodology and technology.In recent years, a large number here of computer vision-based methods have been widely used in surgical workflow recognition tasks.For training the models, a lot of annotated data are necessary.However, the annotation of surgical data requires expert knowledge and thus becomes difficult and time-consuming.

In this paper, we focus on the problem of data deficiency and propose a knowledge transfer learning method based on artificial neural network to compensate a small amount of labeled training data.To solve this problem, we propose an unsupervised method for pre-training a Convolutional-De-Convolutional (CDC) neural network for sequencing surgical workflow frames, which performs neural convolution in space (for semantic abstraction) and neural de-convolution in time (for frame level resolution) simultaneously.Specifically, through neural convolution transfer learning, we only fine-tuned the CDC neural network to classify the surgical phase.We performed some experiments for validating the model, and it showed that the proposed model can effectively extract turbosound ts-18sw700/8a the surgical feature and determine the surgical phase.

The accuracy (Acc), recall, precision (Pres) of our model reached 91.4, 78.9, and 82.5%, respectively.