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Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection

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da Silva, Bruno C. Gregorio ; Ferrari, Ricardo J. ; Gervasi, O ; Murgante, B ; Misra, S ; Garau, C ; Blecic, I ; Taniar, D ; Apduhan, BO ; Rocha, AMAC ; Tarantino, E ; Torre, CM ; Karaca, Y
Número total de Autores: 13
Tipo de documento: Artigo Científico
Fonte: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II; v. 12250, p. 13-pg., 2020-01-01.
Resumo

Among different biological studies, the analysis of leukocyte recruitment is fundamental for the comprehension of immunological diseases. The task of detecting and counting cells in these studies is, however, commonly performed by visual analysis. Although many machine learning techniques have been successfully applied to cell detection, they still rely on domain knowledge, demanding high expertise to create handcrafted features capable of describing the object of interest. In this study, we explored the idea of transfer learning by using pre-trained deep convolutional neural networks (DCNN) as feature extractors for leukocytes detection. We tested several DCNN models trained on the ImageNet dataset in six different videos of mice organs from intravital video microscopy. To evaluate our extracted image features, we used the multiple template matching technique in various scenarios. Our results showed an average increase of 5.5% in the F1-score values when compared with the traditional application of template matching using only the original image information. Code is available at: https://github.com/brunoggregorio/DCNN-feature- extraction. (AU)

Processo FAPESP: 13/26171-6 - Detecção e rastreamento de leucócitos em imagens de microscopia intravital via processamento espaço-temporal
Beneficiário:Bruno César Gregório da Silva
Modalidade de apoio: Bolsas no Brasil - Mestrado
Processo FAPESP: 18/08826-9 - Desenvolvimento de técnicas de engenharia de características e aprendizagem profunda aplicadas à classificação de imagens de ressonância magnética nas classes envelhecimento cognitivo saudável, comprometimento cognitivo leve e Doença de Alzheimer
Beneficiário:Ricardo José Ferrari
Modalidade de apoio: Auxílio à Pesquisa - Regular