Multidimensional data visualization guided by machine learning

• Grant number: 17/12974-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: August 01, 2017
• End date: 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

Abstract

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)

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, AUG 2020. (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, OCT 2021. (14/12236-1, 17/12974-0)