Universal Medical Image Segementation via State Space Model Using AI

This project delivers a Universal Medical Image Segmentation via State Space Model Using AI, aiming to automate the detection and segmentation of organs and tumors in CT and MRI scans through an adaptive deep learning framework. The system integrates State Space Models (SSMs) with advanced neural networks to capture long-range dependencies and spatial correlations across medical images, enabling accurate segmentation of multiple organs within a single unified model. By leveraging semi-supervised learning, CutMix augmentation, and consistency regularization, the approach effectively utilizes both labeled and unlabeled data to enhance robustness and generalization. The model reduces the reliance on large annotated datasets while improving segmentation precision, computational efficiency, and adaptability across diverse imaging modalities. This universal segmentation system aims to assist radiologists by reducing diagnostic workload, accelerating disease detection, and providing accurate, AI- driven insights for clinical and research applications.