Large scale image classification and object detection

Dissertation supervisor: Dr. Tony X. Han.Includes vita.Significant advancement of research on image classification and object detection has been achieved in the past decade. Deep convolutional neural networks have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. However, learning a robust deep CNN model for object recognition is still quite challenging because image classification and object detection is a severely unbalanced large-scale problem. In this dissertation, we aim at improving the performance of image classification and object detection algorithms by taking advantage of deep convolutional neural networks by utilizing the following strategies: We introduce Deep Neural Pattern, a local feature densely extracted from an image with arbitrary resolution using a well trained deep convolutional neural network. We propose a latent CNN framework, which will automatically select the most discriminate region in the image to reduce the effect of irrelevant regions. We also develop a new combination scheme for multiple CNNs via Latent Model Ensemble to overcome the local minima problem of CNNs. In addition, a weakly supervised CNN framework, referred to as Multiple Instance Learning Convolutional Neural Networks is developed to alleviate strict label requirements. Finally, a novel residual-network architecture, Residual networks of Residual networks, is constructed to improve the optimization ability of very deep convolutional neural networks. All the proposed algorithms are validated by thorough experiments and have shown solid accuracy on large scale object detection and recognition benchmarks.Includes bibliographical references (pages 105-119)

An image-classification leveraged object detector

"May 2014."Thesis supervisor: Dr. Tony X. Han.Includes bibliographical references (pages 35-38)

Pharmapolymers in the 21st century: Synthetic polymers in drug delivery applications

The administration of drugs, as a main challenge of pharmaceutical and medicinal applications, has certainly benefited from the application of synthetic polymers. However, despite an enormous effort to develop new materials for drug delivery applications, only few of them have entered the market due to the hurdles of regulation, production, cost efficiency and both industrial's and patients' acceptance. In this review, we summarize all these classes of synthetic polymers, which are on the market as well as the latest developments in clinical trials, and describe their application in polymer-drug conjugates, as excipients, in nano-/microscopic and macroscopic drug carriers, as polymeric coatings, or as polymeric drugs. Our intention is to create a link between the underlying chemical structures, the properties of the polymers, and their area of application, where they are often just known by their trade names or abbreviations. In addition selected types of synthetic polymers are highlighted that feature interesting properties and have the potential to make it to the market in future. (C) 2018 Elsevier B.V. All rights reserved