Authors - Kaja Mohideen A, Senthil Prakash PN Abstract - Breast tumor segmentation using mammographic is a difficult task because mammographic images have low contrasts, complex tissue structures, and high inter patient variability. Radiologists commonly make left-right-breast comparisons to detect suspicious inconsistencies in the image of the left and right breast in the routine clinical practice. It is based on this bilateral diagnostic strategy that this paper suggests a difference-guided bilateral U-Net to inter pretable breast tumor segmentation. Paired left and right mammogram of the same patient are first adjusted by the horizontal flipping and intensity normali zation. A pixel-based difference image is then created to highlight disparities that are absolutely in nature to highlight areas that are asymmetric and which might reflect pathological alterations. To make the network learn both appear ance-based and asymmetry-driven representations, the bilateral mammograms are proposed to be jointly processed with the respective difference map, after which the network will be trained. This design enhances the performance of segmentation without compromising clinical interpretability because it explicit ly points out areas of interest. The suggested method is tested on publicly ac cessible data, such as MIAS and CBIS-DDSM and real-time mammographic images obtained in a clinical setting. The experimental data indicate that differ ence-guided framework provides higher segmentation accuracy and lower false positive rates than single-breast U-Net models, which implies that the frame work can be used to delineate breast tumors on automated mammography.
