Enhancing Medical Image Segmentation with 3D U-Net and Explainable AI for Trustworthy Healthcare

Medical image segmentation is a crucial process for computer-aided diagnosis that uses precise delineation of anatomical structures to aid in clinical decisions. Deep learning models such as 3D U-Net have proven to be the most outstanding performers in the segmentation of volumetric medical images through technologies like CT and MRI scans. However, the lack of interpretability in these models raises concerns about their reliability, and the real-world medical applications would suffer from such issues. In this paper, we propose a 3D U-Net-based seg-mentation framework in conjunction with Explainable AI (XAI) methods to help model transparency and interpretability. We use Grad-CAM map to show the decision-making process of the network thereby making sure that segmentation outputs correctly represent clinically relevant features. We test our concept on publicly available medical imaging datasets, demonstrating how we have improved segmentation accuracy alongside presenting the model’s predictions with meaningful visual explanations. By drawing the link between deep learning efficiency and clinical interpretability, we hope our work will win the public’s trust in AI-assisted diagnostics and thus will make the technology more popular in real-world healthcare settings.