Artificial intelligence in digital breast pathology: Techniques and applications

Abstract

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

Artificial intelligence (AI) and big data in cancer and precision oncology

“…As any other aspect of science in healthcare, diagnostic pathology is adopting the use of digital imaging in pathology. Whole slide imaging (WSI) in the latest innovation in digital pathology [55] . This technology allows viewing of the entire slide as a scanned image with high-resolution images quality and easy storage solution as compared to storage of glass slides ( Fig.…”

Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

“…When there were images captured from clinical cases, the database could have the images with their properties at the same time, which would help in further analysis. As time goes on, the database could grow by itself ( Ibrahim et al, 2020 ). The low-quality images are also a problem for DL analysis.…”

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

“…A number of studies have explored the use of deep learning in mammogram interpretation. DL tried in making a diagnosis of breast pathology in a number of cases, such as differentiating benign from malignant breast masses, separating masses from micro-calcifications, distinguishing between tumor and healthy tissue, discrimination between benign, malignant, and healthy tissue and detect masses in mammogram images [20][21][22][23][24]. AI has potential use in density segmentation and risk calculation, classifying breast tissue into different densities, namely, scattered and uniformly dense breast density categories, image segmentation that is mapping the edges of a lesion, lesion identification, measurement, labeling, comparison with previous images, comparing images from both left and right breasts and also the craniocaudal and mediolateral-oblique view of each breast and breast anatomy classification in mammograms [25][26][27][28].…”

“…These are far-fetched expectations; AI systems have their limitations discussed above. Also, it is crucial to know that AI is good at solving super-specific isolated problems [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. In contrast, humans can understand different concepts, reason and put together vast amounts of information from various aspects and come up with an inclusive decision [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”