Unsupervised deep learning for semantic segmentation using laparoscopic videos: A self-detection and self-learning approach

Abstract

Artificial intelligence (AI) and machine learning methods play a crucial role in image processing applications, particularly in semantic segmentation and localization tasks. These models rely on annotated datasets to train algorithms capable of detecting and localizing objects of interest in images. However, the manual annotation process demands substantial human effort and focus, posing significant challenges in terms of time, economic costs, and energy consumption. This paper introduces a novel unsupervised deep learning approach inspired by the psychology of human learning to address these limitations. First, the self-learning methodology is proposed to utilize only one or two annotated images to train neural networks, enabling automated segmentation and annotation of a large volume of unannotated images within the dataset. Then, to enhance the automation of this process, a complementary object detection algorithm, termed Self-Detection, is proposed. By simply clicking on an object within an image, this algorithm differentiates it from other objects in the scene, streamlining object identification and segmentation. Integrating the proposed Self-Learning and Self-Detection methods results in a fully unsupervised framework for training semantic segmentation neural networks. The key outcomes of this methodology include (1) trained neural network models capable of precise segmentation and localization of objects of interest, and (2) a fully-automatically well-annotated image dataset suitable for training other types of AI models with diverse architectures. The proposed methodology can be used for developing accurate, reliable, and interpretable deep learning models for various tasks and applications, both medical and non-medical, as well as for segmentation or localization tasks.