bibtext: @InProceedings{ Kas_IHRRBDINO_MICCAI2024,
                   author = { Kasem, Mahmoud SalahEldin and Abdallah, Abdelrahman and Abdelhalim, Ibrahim and Alghamdi, Norah Saleh and Contractor, Sohail and El-Baz, Ayman },
                   title = { { IHRRB-DINO: Identifying High-Risk Regions of Breast Masses in Mammogram Images Using Data-Driven Instance Noise (DINO) } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: In this paper, we introduce IHRRB-DINO, an advanced model designed to assist radiologists in effectively detecting breast masses in mammogram images. This tool is specifically engineered to highlight high-risk regions, enhancing the capability of radiologists in identifying breast masses for more accurate and efficient assessments. Our approach incorporates a novel technique that employs Data-Driven Instance Noise (DINO) for Object Localization, which significantly improves breast mass localization. This method is augmented by data augmentation using instance-level noise during the training phase, focusing on refining the model’s proficiency in precisely localizing breast masses in mammographic images. Rigorous testing and validation conducted on the BI-RADS dataset using our model, especially with the Swin-L backbone, have demonstrated promising results. We achieved an Average Precision (AP) of 46.96, indicating a substantial improvement in the accuracy and consistency of breast cancer (BC) detection and localization. These results underscore the potential of IHRRB-DINO in contributing to the advancements in computer-aided diagnosis systems for breast cancer, marking a significant stride in the field of medical imaging technology.
title: IHRRB-DINO: Identifying High-Risk Regions of Breast Masses in Mammogram Images Using Data-Driven Instance Noise (DINO)
authors: Kasem, Mahmoud SalahEldin Abdallah, Abdelrahman Abdelhalim, Ibrahim Alghamdi, Norah Saleh Contractor, Sohail El-Baz, Ayman