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Within this second volume dealing with breast and bladder cancer, the editors and authors detail the latest research related to the application of artificial intelligence (AI) to cancer diagnosis and prognosis and summarize its advantages. It is the intention of the editors and authors to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field. There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to date (to the best of our knowledge) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, with a single book project. Therefore, the purpose of this three-volume work, and particularly for this second volume dealing with breast and bladder cancer, is to present a compendium of these findings related to these two pervasive cancers. Many of the chapter authors are world class researchers in these technologies. Within this coverage it is our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal, leukemia, melanoma, etc.
Preface
Development of artificial neural networks for breast histopathological image analysis
Machine learning in bladder cancer diagnosis
Deep learning in photoacoustic breast cancer imaging
Histopathological breast cancer image classification with feature prioritization using a heuristic algorithm
The use of machine learning and biofluid metabolomics in breast cancer diagnosis
AUTO-BREAST: a fully automated pipeline for breast cancer diagnosis using AI technology
Diagnosis of breast cancer from histopathological images using artificial intelligence
The role of artificial intelligence in the field of bladder cancer
Exploring data science paradigms in breast cancer classification: linking data, learning, and artificial intelligence in medical diagnosis
Automatic detection and classification of invasive ductal carcinoma in histopathology images using convolutional neural networks
Machine learning analysis of breast cancer single-cell omics data
Radiomics, deep learning, and breast cancer detection
Artificial-intelligence-based techniques for the diagnosis of bladder and breast cancer