Synthetic Aperture Radar (SAR) imagery enables high-resolution observation regardless of weather conditions. However, it is difficult to interpret intuitively due to issues such as speckle noise. In contrast, optical imagery provides realistic visual information but is vulnerable to weather and illumination changes. Leveraging this complementarity, SAR-to-Optical image translation has attracted considerable attention. Nevertheless, existing paired learning approaches are limited by the high cost of large-scale data collection and residual misregistration errors, while unpaired learning approaches are prone to global color shifts and structural distortions. To address these limitations, this study proposes a SAR-to-Optical translation framework that introduces a weight map throughout the training process. The proposed weight map combines Attribution maps and Uncertainty maps to amplify losses in important regions while guiding conservative learning in uncertain areas. Moreover, the weight map is incorporated into the registration stage to refine pixel-wise displacement estimation, preserve boundary and structural consistency, and enhance overall training stability. The experimental results demonstrate that the proposed method outperforms existing approaches on the SAR2Opt and SEN1-2 datasets in all metrics, including PSNR and SSIM.

Pathological complete response (pCR) is a critical prognostic indicator in breast cancer treatment. Reliable prediction of pCR prior to therapy can prevent unnecessary toxicities and facilitate optimal clinical decision-making. We propose Contrastive MammoPatch-Net (CMP-Net), a novel multiple-instance learning framework that leverages hierarchical multi-view mammography. Extensive experiments demonstrate that CMP-Net achieves superior performance compared to existing baseline approaches.