Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Debnath, Rimpi; Paul, Jeno Lovesum Selvaraj; Thiyagarajan, Vigneshwaran Title A name given to the resource Advancements and challenges in deep learning for breast cancer screening: A review Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20267; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0298759" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0298759</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028311957?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028311957?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Debnath R., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Paul J.L.S., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Thiyagarajan V., Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India Description An account of the resource Breast cancer continues to be the prevalent cancer on a global scale, playing a major role in the worldwide cancer statistics, the critical role of early detection in reducing death rates is underscored. In the context of breast cancer, screening, deep learning (DL) emerged as a game-changer, providing notable improvements over existing techniques. This review explores the use of DL in analysing images from various sources such as X-rays, ultrasound, magnetic resonance imaging, and biopsies. Additionally, it highlights DL's potential to pre-screen for cancer by integrating diverse data, including demographic information, biological markers, and meta-analytical risk assessments. The analysis reveals that deep learning frameworks, especially those optimized with feature selection techniques, attain the minimal false-negative rates, effectively distinguishing between patients with and without cancer. Notably, DL models demonstrate lower prediction uncertainty compared to traditional machine learning, as shown by reduced standard deviations in performance metrics. Additionally, the paper proposes a cascade network model that achieves 98.61% classification accuracy and a 98.41% F1 score by segmenting tumours with a UNet architecture and classifying them with a ResNet backbone. Despite these advancements, challenges such as limited annotated data and adaptability to new data domains persist. In response to these issues, the proposed Self AdaptNet leverages innovative self-supervised learning and adversarial techniques to improve the resilience as well as adaptability of BC detection models.AI technology, particularly DL-based systems, has the capacity to completely transform breast cancer screening by improving screening accuracy and reducing observer variability. However, clinical adoption requires standardized guidelines, trustworthy AI practices, and collaboration among researchers, clinicians, and regulatory bodies. 2026 Author(s). Subject The topic of the resource Breast cancer screening; Deep learning (DL); Feature selection; Self-supervised learning; UNet architecture Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online