Med-DualNet: Enhancing Medical Image Segmentation with Hard-Patch Mining and Joint Error Adjustment

Automated segmentation of organs and lesions is crucial for effective treatment design and care planning in the medical field. However, accurately segmenting organs and lesions is challenging due to the complex distribution of lesions within organs, such as cases with early diseases characterized by small volumes and blurred boundaries. In this work, we propose Med-DualNet, a dual-branch segmentation network that incorporates Hard-Patch Mining and Joint Error Adjustment strategies to address these challenges. Branch I focuses on organ segmentation, while Branch II targets challenging lesion segmentation using a two-stage network and dedicated strategies. The Hard-Patch Mining strategy in Stage-one dynamically generates a subset of image patches that highlight challenging aspects of lesion segmentation, including small volumes and blurred boundaries. Furthermore, the Joint Error Adjustment strategy in Stage-two facilitates the learning of challenging lesion features. Experimental results on three public medical datasets demonstrate the improved performance of the proposed model in organ and lesion segmentation, as measured by visual and quantitative metrics.