Multi-Scale Adaptive Transformer Enhanced Deep Neural Network for Advanced Image Analysis in Regenerative Science

Accurate analysis of complex imaging data is crucial in regenerative science, where precision is essential. However, challenges such as noise, anatomical variations, and low contrast regions hinder effective image interpretation. This paper introduces MATHSegNet, a Multi-Scale Adaptive Transformer-Enhanced Deep Neural Network, designed to enhance image analysis efficiency and accuracy. MATHSegNet integrates CNNs for fine-grained local feature extraction with Transformers to capture global dependencies and spatial relationships. Multi-scale feature extraction ensures precise representation at different spatial levels, while attention mechanisms highlight key regions for improved analysis. A hybrid loss function combining Dice Loss and Unified Focal Loss effectively addresses class imbalance, improving segmentation of smaller structures. Developed using PyTorch and TensorFlow, MATHSegNet offers fast training and adaptability. Experimental results demonstrate a 7–10% improvement over existing models, validated using metrics such as Dice Similarity Coefficient, IoU, Sensitivity, and Specificity, making MATHSegNet a scalable and interpretable solution for regenerative imaging tasks.