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Publicações

2019

Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

Autores
Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

Publicação
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Wide Residual Network for Lung-Rads™ Screening Referral

Autores
Ferreira, CA; Aresta, G; Cunha, A; Mendonça, AM; Campilho, A;

Publicação
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

Abstract

2019

Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Autores
Ferreira, CA; Penas, S; Silva, J; Mendonça, AM;

Publicação
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

Abstract

2018

Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

Autores
Ferreira, CA; Melo, T; Sousa, P; Meyer, MI; Shakibapour, E; Costa, P; Campilho, A;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Breast cancer is one of the leading causes of female death worldwide. The histological analysis of breast tissue allows for the differentiation of the tissue suspected to be abnormal into four classes: normal tissue, benign tumor, in situ carcinoma and invasive carcinoma. Automatic diagnostic systems can help in that task. In this sense, this work propose a deep neural network approach using transfer learning to classify breast cancer histology images. First, the added top layers are trained and a second fine-tunning is done on some feature extraction layers that are frozen previously. The used network is an Inception Resnet V2. In order to overcome the lack of data, data augmentation is performed too. This work is a suggested solution for the ICIAR 2018 BACH-Challenge and the accuracy is 0.76 in the blind test set. © 2018, Springer International Publishing AG, part of Springer Nature.