Browsing C-BER - Indexed Articles in Conferences by Author "6835"
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ItemClassification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2( 2018) Carlos Alexandre Ferreira ; Tânia Fernandes Melo ; Sousa,P ; Maria Inês Meyer ; Elham Shakibapour ; Costa,P ; Aurélio Campilho ; 7034 ; 7124 ; 7181 ; 6835 ; 6071Breast 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.
ItemDeep Convolutional Artery/Vein Classification of Retinal Vessels( 2018) Maria Inês Meyer ; Adrian Galdran ; Costa,P ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6071 ; 6381 ; 6835The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of 96 % on large caliber vessels, and an overall accuracy of 84 %. © 2018, Springer International Publishing AG, part of Springer Nature.
ItemA Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection( 2018) Maria Inês Meyer ; Adrian Galdran ; Ana Maria Mendonça ; Aurélio Campilho ; 6381 ; 6071 ; 6825 ; 6835