Introducing elements of fractal geometry into deep convolutional neural networks: an application to the recognition and categorization of Lung Cancer

• Grant number: 20/01984-8
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2020
• End date: August 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Principal Investigator: Joao Batista Florindo
• Grantee: Joao Batista Florindo
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated researchers: Estevão Esmi Laureano ; Gabriel Landini ; Gwanggil Jeon ; Konradin Metze ; Kyungkoo Jun ; Odemir Martinez Bruno ; Peter Sussner

Abstract

This project proposes the study and development of a computational methodology for the analysis of images introducing elements of fractal theory into deep convolutional neural networks. Although these networks have become ubiquitous in computer vision, and in particular in the analysis of medical images, classical theories such as fractal geometry can still be very useful, among other reasons, for allowing a modeling with more direct interpretation and not need so much data for training. In this context, we propose the introduction of theoretical and technical concepts of fractal geometry into the pipeline of convolutional neural networks. The process takes place under three perspectives: the input, output and the network architecture. As for the input, a multifractal transformation is applied to the original image. At the output, the fractal dimension and/or multifractal spectrum of the convolutional layer feature maps will be calculated. Both solutions will be implemented directly in the network layers, through convolutions and other operators. With regards to the architecture, self-similar information flows will be used replacing residual architectures. The developed methodology will be applied to a problem in the medical area, which is the identification and categorization of lung cancer sub-types from microscopic images, using both histological and cytological samples. We expect that the results obtained will represent important theoretical and practical advances. In theoretical terms, the proposal aims to investigate the possibility of improvements in automatic learning algorithms for image classification, both in the classification accuracy, as well as in the lower sensitivity to a smaller amount of training data. In practical terms, useful implications for society are expected, through a better understanding of carcinogenic processes in this way promoting possibilities for both an earlier diagnosis and more effective treatment, improving the life quality and expectancy of the patient . (AU)

Scientific publications

(8)

(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)

FLORINDO, JOAO BATISTA; FARINELLA, GM; RADEVA, P; BOUATOUCH, K. Wavelet Transform for the Analysis of Convolutional Neural Networks in Texture Recognition. PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 4, v. N/A, p. 8-pg., 2022-01-01. (20/01984-8)

METZE, KONRADIN; MORANDIN-REIS, ROSANA C.; LORAND-METZE, IRENE; FLORINDO, JOAO B.. The Amount of Errors in ChatGPT's Responses is Indirectly Correlated with the Number of Publications Related to the Topic Under Investigation. ANNALS OF BIOMEDICAL ENGINEERING, v. 51, n. 7, p. 2-pg., 2023-04-15. (20/01984-8)

FLORINDO, JOAO; BRUNO, ODEMIR MARTINEZ; IEEE. Using fractal interpolation over complex network modeling of deep texture representation. 2022 ELEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), v. N/A, p. 5-pg., 2022-01-01. (20/01984-8)

FLORINDO, JOAO B.; ABREU, EDUARDO. A pseudo-parabolic diffusion model to enhance deep neural texture features. MULTIMEDIA TOOLS AND APPLICATIONS, v. N/A, p. 22-pg., 2023-06-29. (20/01984-8, 20/09838-0)

DE SA, LUIZ ALBERTO PEREIRA; ZIELINSKI, KALLIL M. C.; RODRIGUES, ERICK OLIVEIRA; BACKES, ANDRE R.; FLORINDO, JOAO B.; CASANOVA, DALCIMAR. A novel approach to estimated Boulingand-Minkowski fractal dimension from complex networks. CHAOS SOLITONS & FRACTALS, v. 157, p. 10-pg., 2022-04-01. (20/01984-8)

FLORINDO, JOAO; METZE, KONRADIN. Using Non-Additive Entropy to Enhance Convolutional Neural Features for Texture Recognition. Entropy, v. 23, n. 10, OCT 2021. (20/01984-8, 20/09838-0)

DE MATTOS, AMILCAR CASTRO; FLORINDO, JOAO BATISTA; ADAM, RANDALL L.; LORAND-METZE, IRENE; METZE, KONRADIN. The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival. Microscopy and Microanalysis, v. 28, n. 2, p. 5-pg., 2022-02-23. (20/01984-8, 20/09838-0)

ROCHA, MARINA M. M.; LANDINI, GABRIEL; FLORINDO, JOAO B.. Medical image classification using a combination of features from convolutional neural networks. MULTIMEDIA TOOLS AND APPLICATIONS, v. N/A, p. 24-pg., 2022-11-15. (20/01984-8)