Modular Neural Network Approaches for Surgical Image Recognition
Nosseiba Ben Salem Younes Bennani Joseph Karkazan Abir Barbara Charles Dacheux Thomas Gregory
Deep learning-based applications have seen a lot of success in recent years.
Text, audio, image, and video have all been explored with great success using
deep learning approaches. The use of convolutional neural networks (CNN) in
computer vision, in particular, has yielded reliable results. In order to
achieve these results, a large amount of data is required. However, the dataset
cannot always be accessible. Moreover, annotating data can be difficult and
time-consuming. Self-training is a semi-supervised approach that managed to
alleviate this problem and achieve state-of-the-art performances. Theoretical
analysis even proved that it may result in a better generalization than a
normal classifier. Another problem neural networks can face is the increasing
complexity of modern problems, requiring a high computational and storage cost.
One way to mitigate this issue, a strategy that has been inspired by human
cognition known as modular learning, can be employed. The principle of the
approach is to decompose a complex problem into simpler sub-tasks. This
approach has several advantages, including faster learning, better
generalization, and enables interpretability.
  In the first part of this paper, we introduce and evaluate different
architectures of modular learning for Dorsal Capsulo-Scapholunate Septum (DCSS)
instability classification. Our experiments have shown that modular learning
improves performances compared to non-modular systems. Moreover, we found that
weighted modular, that is to weight the output using the probabilities from the
gating module, achieved an almost perfect classification. In the second part,
we present our approach for data labeling and segmentation with self-training
applied on shoulder arthroscopy images.