Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Automated summary

Incorporating scientific research into clinical practice through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, improves patient treatment. Computational pathology is a rapidly growing subspecialty that has the potential to integrate whole slide images, multi-omics data, and health informatics for diagnosing breast cancer.