MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation

TL;DR

MAPUNetR combines vision transformers with U-Net to improve medical image segmentation accuracy, resolution preservation, and interpretability, demonstrating strong results on benchmark datasets.

Contribution

This paper introduces MAPUNetR, a hybrid architecture that integrates transformers with U-Net to enhance segmentation performance and interpretability in medical imaging.

Findings

Achieved a dice score of 0.88 on BraTS 2020

Attained a dice coefficient of 0.92 on ISIC 2018

Maintains stable performance across datasets

Abstract

Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personalized patient care. However, developing neural networks for segmentation remains challenging, especially when preserving image resolution, which is essential in detecting subtle details that influence diagnoses. Moreover, the lack of transparency in these deep learning models has slowed their adoption in clinical practice. Efforts in model interpretability are increasingly focused on making these models' decision-making processes more transparent. In this paper, we introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Evaluated on the BraTS 2020 dataset, MAPUNetR achieved a dice score of 0.88 and a dice coefficient of 0.92 on the ISIC 2018 dataset. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice.