Fusion of Real and Synthetic Subtracted Contrast-Enhanced Mammograms for Enhanced Tumor Detection

Contrast-enhanced spectral mammography (CESM) has emerged as a valuable tool for detecting breast tumors, offering enhanced sensitivity in specific clinical scenarios. However, accurate segmentation of tumors based on CESM imaging modality remains challenging. In this work, we propose a novel approach to utilizing generative modeling in the context of CESM for breast cancer diagnosis. We utilize paired imageto-image translation to generate high-fidelity dual-energy subtracted (DES) representations that closely align with real data. Our proposed model integrates two inputs: real DES images and synthesized ones, aiming to enhance segmentation accuracy and generate masks pinpointing tumor locations. Following segmentation, these masks are classified to distinguish between benign and malignant tumors. A Multi Scale Subtraction Network is utilized to improve tumor detection with the integration of DenseLossNet, a novel loss function based to assist in refining segmentation results, thereby achieving precise delineation of tumor boundaries. Through rigorous evaluation, our proposed framework demonstrates promising results in tumor detection and classification, achieving high accuracy in distinguishing between benign and malignant tumors while precisely localizing tumor positions. These findings underscore the potential of our approach in improving tumor detection in CESM, offering clinicians valuable insights for more informed decision making.