Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data
Beibit Abdikenov, Tomiris Zhaksylyk, Olzhas Shortanbaiuly, Yerzhan Orazayev, Nursultan Makhanov, Temirlan Karibekov, Victor Suvorov, Aruzhan Imasheva, Kurmash Zhumagozhayev, Aigerim Seitova
Abstract
Breast cancer diagnosis has greatly benefited from the use of deep learning (DL) and machine learning (ML) methods on multimodal data, including mammography, magnetic resonance imaging (MRI), ultrasound, histopathology, genomic data, and clinical reports. Every modality presents its challenges and opportunities, demanding a thorough understanding of used methods for accurate and early diagnosis. Our review provides an exhaustive survey of the latest advances in DL and ML applications to detect, classify, and segment breast cancer in these modalities. Our work also addresses basic issues of data limitations, model generalizability, and interpretability. To overcome these issues we explore the state-of-the-art Vision-Language Models (VLMs) as an emerging approach for multimodal learning. Through incorporation of the current advances and identification of areas of future improvement, this review is a precious source of information to researchers and practitioners. The findings highlight the importance of interdisciplinary collaboration, application of standardized data sets, and continuous improvement of the methodology to facilitate clinical uptake of AI-based breast cancer diagnostic systems.