This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

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Explanations have been shown to increase the user's trust in recommendations in addition to providing other benefits such as scrutability, which is the ability to verify the validity of recommendations. Most explanation methods are designed for classical neighborhood-based Collaborative Filtering (CF) or rule-based methods. For the state of the art Matrix Factorization (MF) recommender systems, recent explanation methods, require an additional data source, such as item content data, in addition to rating data. In this paper, we address the case where no such additional data is available and propose a new Explainable Matrix Factorization (EMF) technique that computes an accurate topn recommendation list of items that are explainable. We also introduce new explanation quality metrics, that we call Mean Explainability Precision (MEP) and Mean Explainability Recall (MER).

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Studies for providing me the opportunity to pursue my degree and for their unfailing support and assistance throughout my graduate studies. I would also like to thank the Kentucky Science and Engineering Foundation for partially providing the funding for the work. My sincere thanks goes to my fellow lab-mates, with a special mention to Wenlong Sun and Gopi Chand Nutakki. Our conversations on research and their good-hearted support and friendship will not be forgotten. Last but by no means least, I am so grateful to my family and friends who have supported me in every possible way unconditionally and selflessly. Many thanks to my sister Behnaz Abdollahi for encouraging me and expressing confidence in my abilities when I could only do the opposite. Moreover, thanks to Milad Nikoukar for motivating me to keep reaching for excellence. iii