R3Net: Recursive Residual Refinement Network Architecture for Decoder‐Free Medical Image Segmentation

Background Medical image segmentation methods based on encoder–decoder architectures often achieve high accuracy but typically require substantial computational resources and contain redundant parameters. Purpose This study aims to develop an efficient decoder‐free segmentation framework that maintains competitive performance by strengthening the encoding process. Methods We propose R3Net, an encoder‐only segmentation architecture based on a recursive residual refinement (R3) mechanism. By recursively reusing encoder stages and progressively fusing multiscale features through residual pathways, R3Net reconstructs high‐resolution features without requiring a dedicated decoder. Results Experiments on three medical imaging modalities—cardiac MRI (Automated Cardiac Diagnosis Challenge), abdominal CT (Synapse), and thyroid ultrasound (Thyroid Nodule Multimodal Learning)—demonstrate that R3Net achieves segmentation performance comparable to representative encoder–decoder models while reducing the number of model parameters and computational complexity. Conclusion R3Net provides an effective decoder‐free alternative for medical image segmentation, suggesting that competitive dense prediction can be achieved through recursive refinement within the encoder.