El-Baz A. Artificial Intelligence...Cancer Diagnosis...Prognosis Vol 1. 2022

Textbook in PDF format

Within this first volume dealing with lung and kidney 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, within a single book project. Therefore, the purpose of this three-volume work, and particularly for this first volume dealing with lung and kidney cancer, is to present a compendium of these findings related to these two pervasive cancers. 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
American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model
Neural-ensemble-based detection: a modern way to diagnose lung cancer
Computed tomography and magnetic resonance imaging machine learning applications for renal cell carcinoma
Pulmonary nodule-based feature learning for automated lung tumor grading using convolutional neural networks
Detection of lung contours using closed principal curves and machine learning
Bytes, pixels, and bases: machine learning in imaging–omics for renal cell carcinoma
Detection, growth quantification, and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
Training a deep multiview model using small samples of medical data
Overview of deep learning for lung cancer diagnosis
Artificial intelligence for cancer diagnosis
Lung cancer diagnosis using 3D-CNN and spherical harmonics expansions