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In order to obtain an accurate diagnosis from brain magnetic resonance imaging (MRI) scans, automatic methods have been proposed to decrease the manpower and human error of a radiologist's diagnosis. In this study, to improve the performance of the computer-aided diagnosis, we proposed a novel scheme to transform the issue of a decision where the image data is a set of discrete observations into an optimization problem where the image data was a set of functions. We showed that the values of the classification accuracy and kappa ranking measure increased significantly further than those for the univariate classifiers with a small number of samples. These results suggested that the improved performance was mainly due to an isolated class of classification methods. We also proposed a new scheme to select the number of training samples to reduce the computational complexity. This scheme aimed to remove the influence of the given training samples on the performance of the classification method by dampening the classifier's influence based on the extent of the training samples in the given dataset. The method is simple to implement than other existing algorithms and showed significant performance. Furthermore, we effectively reduced the computational time that was needed to perform the stoechiometric inference with a small number of training samples.
This book is a compendium of the proceedings of the conference. It has five chapters: 1) Introductory chapter with previous studies in the past 25 years. 2) Biomarkers and Biomarker Research in the Post-cancer Era. 3) Adjuvant Therapies in Pancreatic Cancer. 4) Opportunities and Challenges for Translational Research in Pancreatic Cancer. 5) Pancreatic Disease Mechanisms and Modeling Pancreatic Cancer, and 6) Pancreatic Disease Models and Imaging. Read more...
As a follow-up to a book of Apreganzia and Tuma, the book in this series, edited by Beclin, Stern, and Morgenroth, examines advanced concepts and practices in current cancer care, including the use of emerging non-chemotherapeutic anticancer agents and diagnostics. The text includes relevant clinical and preclinical research, focus on high quality historial and medical practices, and algorithms for high-quality patient care. d2c66b5586
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