Cross-modal hybrid architectures for gastrointestinal tract image analysis: A systematic review and futuristic applications

This review paper presents an in-depth exploration of gastrointestinal (GI) tract image analysis, particularly emphasizing organ and polyp segmentation. It addresses the inherent challenges posed by the GI tract's complex anatomy and diverse pathologies, which complicate accurate image analysis. Central to this review is the examination of hybrid computational models that integrate convolutional neural networks (CNNs) and Transformers. This synergy enhances the accuracy of segmenting intricate structures in GI tract imaging, marking a significant advancement in the field. A notable contribution of this review is the systematic categorization and analysis of the latest methodologies in organ and polyp segmentation. It provides a comprehensive overview of various techniques, highlighting their strengths and limitations in addressing the specifications of GI tract imaging. This survey serves as a valuable reference for researchers, outlining current practices and offering insights for future innovations. The review also underscores the critical role of extensive and diverse datasets in advancing GI tract image analysis. It stresses the need for high-quality datasets to effectively train and evaluate emerging models, addressing the broad spectrum of GI tract conditions. Moreover, the review delves into the burgeoning area of Generative AI, exploring its potential to enrich datasets and enhance segmentation models. Future developments in GI tract segmentation will focus on refining hybrid CNN-Transformer models and creating larger, more diverse datasets for better model training. Specialized focus on specific segmentation challenges, like polyp and organ segmentation, is anticipated. The field will explore Generative AI applications for innovative segmentation approaches. Collaborative efforts between technologists and clinicians will enhance practical clinical integration and applicability.