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Multidimensional data visualization guided by machine learning

Grant number: 17/12974-0
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): August 01, 2017
Effective date (End): July 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Alexandre Xavier Falcão
Grantee:Daniel Osaku
Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM


This project will address two machine learning problems: (I) feature learning and (II) active learning. In both cases, the strategy is to involve the expert in the machine learning loop by means of visual analytics tools. In (I), the expert shall be able to understand and intervene in the deep learning process in order to design more effective image descriptors. In (II), the expert shall be able to select key examples for label supervision and design of an image classifier. The diagnosis of intestinal parasites in microscopy images is the main application, but the validation of the techniques will involve other types of images. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
OSAKU, D.; CUBA, C. F.; SUZUKI, C. T. N.; GOMES, J. F.; FALCAO, A. X.. Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits. COMPUTERS IN BIOLOGY AND MEDICINE, v. 123, . (14/12236-1, 17/12974-0)
OSAKU, D.; GOMES, J. F.; FALCAO, A. X.. Convolutional neural network simplification with progressive retraining. PATTERN RECOGNITION LETTERS, v. 150, p. 235-241, . (14/12236-1, 17/12974-0)

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