Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.

A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications

Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stands out for its ability to effectively combine modality-specific features during the learning process. This systematic review aims to comprehensively analyze and formalize current intermediate fusion methods in biomedical applications. We investigate the techniques employed, the challenges faced, and potential future directions for advancing intermediate fusion methods. Additionally, we introduce a structured notation to enhance the understanding and application of these methods beyond the biomedical domain. Our findings are intended to support researchers, healthcare professionals, and the broader deep learning community in developing more sophisticated and insightful multimodal models. Through this review, we aim to provide a foundational framework for future research and practical applications in the dynamic field of MDL.

A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations this work proposes to use deep generative models for virtual contrast enhancement on CESM, aiming to make the CESM contrast-free as well as to reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model's performance, also exploiting radiologists' assessments, on a novel CESM dataset that includes 1138 images that, as a further contribution of this work, we make publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.