Exploring Transfer Learning via Convolutional Neural Networks for Image Classification and Super-Resolution

• Ano de defesa: 2018
• Autor(a) principal: Ribeiro, Eduardo Ferreira
• Orientador(a): Uhl, Andreas
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade de Salzburg; Salzburg
• Programa de Pós-Graduação: Faculty of Natural Sciences
• País: Áustria
• Palavras-chave em Português: Transfer Learning; Deep Learning; Super-Desolution; Image classification
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://hdl.handle.net/11612/1009

This work presents my research about the use of Convolutional Neural Network (CNNs) for transfer learning through its application for colonic polyp classification and iris super-resolution. Traditionally, machine learning methods use the same feature space and the same distribution for training and testing the tools. Several problems in this approach can emerge as, for example, when the number of samples for training (especially in a supervised training) is limited. In the medical field, this problem is recurrent mainly because obtaining a database large enough with appropriate annotations for training is highly costly and may become impractical. Another problem relates to the distribution of textural features in a image database which may be too large such as the texture patterns of the human iris. In this case a single and specific training database might not get enough generalization to be applied to the entire domain. In this work we explore the use of texture transfer learning to surpass these problems for two applications: colonic polyp classification and iris super-resolution. The leading cause of deaths related to intestinal tract is the development of cancer cells (polyps) in its many parts. An early detection (when the cancer is still at an early stage) can reduce the risk of mortality among these patients. More specifically, colonic polyps (benign tumors or growths which arise on the inner colon surface) have a high occurrence and are known to be precursors of colon cancer development. Several studies have shown that automatic detection and classification of image regions which may contain polyps within the colon can be used to assist specialists in order to decrease the polyp miss rate. However, the classification can be a difficult task due to several factors such as the lack or excess of illumination, the blurring due to movement or water injection and the different appearances of polyps. Also, to find a robust and a global feature extractor that summarizes and represents all these pit-patterns structures in a single vector is very difficult and Deep Learning can be a good alternative to surpass these problems. One of the goals of this work is show the effectiveness of CNNs trained from scratch for colonic polyp classification besides the capability of knowledge transfer between natural images and medical images using off-the-shelf pretrained CNNs for colonic polyp classification. In this case, the CNN will project the target database samples into a vector space where the classes are more likely to be separable. The second part of this work dedicates to the transfer learning for iris super-resolution. The main goal of Super-Resolution (SR) is to produce, from one or more images, an image with a higher resolution (with more pixels) at the same time that produces a more detailed and realistic image being faithful to the low resolution image(s). Currently, most iris recognition systems require the user to present their iris for the sensor at a close distance. However, at present, there is a constant pressure to make that relaxed conditions of acquisitions in such systems could be allowed. In this work we show that the use of deep learning and transfer learning for single image super resolution applied to iris recognition can be an alternative for Iris Recognition of low resolution images. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process.