Soft Tissue Sarcoma Segmentation Network Based on Ensemble Learning

Accurate segmentation of soft tissue sarcoma in medical images is crucial for effective diagnosis and treatment planning. This study aims to improve soft tissue sarcoma segmentation by proposing a two-layer ensemble model that leverages diverse neural network architectures and a confidence-based integration method. In the first layer, we employ prominent models like CaraNet, AttUNet(M), UNet(S), UNet(M), U Transformer, and Swin UNet for initial segmentation. The second layer utilizes a confidence-based ensemble approach to fuse the outputs of the first layer models, enhancing segmentation accuracy. Experimental evaluation on axial and coronal datasets reveals substantial enhancements in Dice coefficient, sensitivity, and specificity, highlighting the effectiveness of our ensemble strategy. Our approach outperforms individual base learners and ensures smoother and more accurate soft tissue sarcoma segmentation. The proposed two-layer ensemble model, integrating diverse neural network models and a confidence-based ensemble technique, offers improved soft tissue sarcoma segmentation results. This approach holds promise for enhancing diagnostic accuracy and aiding medical decision-making in sarcoma cases.