A Novel Approach to Medical Image Segmentation with Convformer-Based Attention Mechanism and UNet

Abstract

Accurate segmentation of medical images is a critical task in the field of healthcare, aiding in precise diagnosis and effective treatment planning. This project explores the enhancement of image segmentation models through the integration of advanced attention mechanisms. Our primary objective is to compare various attention techniques to develop a lightweight yet highly accurate model suitable for real-time applications. Given the significant body of work in medical image segmentation, our approach seeks to balance accuracy with computational efficiency. By incorporating different attention mechanisms and rigorously evaluating their performance, we aim to identify the optimal strategy for improving segmentation outcomes. The results demonstrate the potential for improved segmentation accuracy and efficiency, highlighting the effectiveness of attention-based models in capturing intricate patterns and dependencies within medical imaging data. We found out in our work that the CNN-based attention mechanism, or Convformer, effectively overcomes the issues related to the training conflict between CNNs and transformers. This project sets the groundwork for future advancements in semi-supervised and weakly-supervised learning, and we plan to expand our model’s applicability across a broader range of medical imaging scenarios. Our ultimate objective is to contribute towards the development of robust, efficient, and adaptable segmentation models that can enhance diagnostic accuracy and patient care in various medical fields.