Breast cancer remains one of the leading causes of mortality among women worldwide, with early detection being crucial for effective treatment and improved survival rates. Conventional diagnostic methods, including mammography and histopathological analysis, offer critical insights but are limited by subjective interpretation, low sensitivity in dense tissues, and labor-intensive evaluation processes. Recent advances in artificial intelligence (AI), particularly deep learning, have demonstrated exceptional potential in automating and enhancing breast cancer detection through feature extraction, classification, and predictive modeling. This chapter presents a comprehensive exploration of multimodal deep learning approaches that integrate histopathological and mammographic data to improve diagnostic accuracy, reliability, and clinical interpretability. Emphasis was placed on convolutional neural networks (CNNs), hybrid architectures, attention mechanisms, and explainable AI (XAI) techniques to highlight relevant pathological and anatomical features. The chapter addresses challenges such as imbalanced datasets, rare cancer subtypes, heterogeneous imaging protocols, and cross-modal alignment, proposing robust strategies for overcoming these limitations. Evaluation frameworks incorporating quantitative metrics and clinical validation are discussed to ensure model transparency, reproducibility, and trustworthiness. By leveraging multimodal information and advanced AI strategies, the proposed methodologies facilitate early-stage detection, reduce diagnostic errors, and support precision medicine initiatives. This work underscores the transformative impact of AI-driven multimodal imaging on breast cancer diagnostics, offering a pathway for the development of scalable, accurate, and interpretable clinical tools.
Breast cancer remains a predominant global health challenge, representing one of the leading causes of cancer-related mortality among women worldwide [1]. Its early detection was pivotal for improving treatment outcomes and reducing mortality rates. Conventional diagnostic approaches primarily include mammography, which visualizes macro-level breast tissue structures, and histopathological analysis, which provides micro-level cellular and tissue information [2]. Mammography allows identification of masses, microcalcifications, and architectural distortions, whereas histopathology offers detailed insights into cellular morphology, nuclear atypia, and tissue grading [3]. Their clinical importance, these modalities are limited by factors such as low sensitivity in dense breast tissue, subjective interpretation, and labor-intensive processing. Inter- and intra-observer variability further compromise consistency, leading to potential delays or misdiagnosis. These challenges highlight the need for automated, precise, and reliable diagnostic frameworks capable of integrating the complementary strengths of histopathology and mammography [4]. Recent advances in computational methods, particularly artificial intelligence (AI) and deep learning, provide a promising solution for overcoming these limitations. By automating feature extraction, pattern recognition, and classification, AI offers a potential paradigm shift in breast cancer detection and diagnosis [5].
The application of deep learning techniques, including convolutional neural networks (CNNs) and hybrid architectures, has demonstrated substantial success in medical imaging [6]. These models are capable of learning hierarchical representations from raw image data, capturing both subtle and complex patterns that may not be perceptible to human observers. In histopathological images, CNNs can identify variations in nuclear size, shape, and arrangement, while in mammograms, they detect structural abnormalities and suspicious lesions [7]. Transfer learning, using pre-trained networks such as ResNet, VGG, or DenseNet, allows models to leverage knowledge from large-scale image datasets, overcoming the limitations posed by the scarcity of annotated medical images [8]. Patch-based analysis has been effectively employed to manage high-resolution images, preserving critical details while reducing computational complexity [9]. By incorporating hybrid models that combine deep learning with traditional machine learning algorithms, predictive performance, generalization, and robustness can be further improved. These advancements indicate that deep learning can significantly enhance both the sensitivity and specificity of breast cancer detection [10].
Multimodal integration of histopathological and mammographic data represents an essential advancement in AI-based breast cancer diagnostics [11]. Each modality provides unique, complementary information: histopathology captures micro-level tissue and cellular features, while mammography offers macro-level structural insights [12]. By fusing these modalities, models can overcome the limitations inherent to single-modality analysis, such as missed lesions in dense breast tissue or subtle cellular abnormalities [13]. Feature-level fusion allows the combination of discriminative patterns extracted from both data types into a unified representation, while decision-level fusion integrates predictions from modality-specific models to improve overall reliability [14]. Attention mechanisms within multimodal networks further enhance the ability to focus on diagnostically relevant regions, enabling accurate identification of malignant lesions and early-stage tumors. These integrated approaches not only improve classification performance but also contribute to more comprehensive and contextually relevant diagnostic insights [15].
