C-BER - Indexed Articles in Conferences

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Browsing C-BER - Indexed Articles in Conferences by Author "6071"
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    Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images
    ( 2022) Joana Maria Rocha ; Sofia Cardoso Pereira ; João Manuel Pedrosa ; Aurélio Campilho ; Ana Maria Mendonça ; 6071 ; 6381 ; 7623 ; 7800 ; 8251
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    Chest Radiography Few-Shot Image Synthesis for Automated Pathology Screening Applications
    ( 2021) Sousa,MQE ; João Manuel Pedrosa ; Joana Maria Rocha ; Sofia Cardoso Pereira ; Ana Maria Mendonça ; Aurélio Campilho ; 7800 ; 8251 ; 6071 ; 6381 ; 7623
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    Classification 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 ; 6071
    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.
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    Creation of Retinal Mosaics for Diabetic Retinopathy Screening: A Comparative Study
    ( 2018) Tânia Fernandes Melo ; Ana Maria Mendonça ; Aurélio Campilho ; 6381 ; 7124 ; 6071
    The creation of retinal mosaics from sets of fundus photographs can significantly reduce the time spent on the diabetic retinopathy (DR) screening, because through mosaic analysis the ophthalmologists can examine several portions of the eye at a single glance and, consequently, detect and grade DR more easily. Like most of the methods described in the literature, this methodology includes two main steps: image registration and image blending. In the registration step, relevant keypoints are detected on all images, the transformation matrices are estimated based on the correspondences between those keypoints and the images are reprojected into the same coordinate system. However, the main contributions of this work are in the blending step. In order to combine the overlapping images, a color compensation is applied to those images and a distance-based map of weights is computed for each one. The methodology is applied to two different datasets and the mosaics obtained for one of them are visually compared with the results of two state-of-the-art methods. The mosaics obtained with our method present good quality and they can be used for DR grading. © 2018, Springer International Publishing AG, part of Springer Nature.
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    Deep Convolutional Artery/Vein Classification of Retinal Vessels
    ( 2018) Maria Inês Meyer ; Adrian Galdran ; Costa,P ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6071 ; 6381 ; 6835
    The 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.
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    End-to-End Supervised Lung Lobe Segmentation
    ( 2018) Ferreira,FT ; Sousa,P ; Adrian Galdran ; Sousa,MR ; Aurélio Campilho ; 6825 ; 6071
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    A No-Reference Quality Metric for Retinal Vessel Tree Segmentation
    ( 2018) Adrian Galdran ; Costa,P ; Bria,A ; Teresa Finisterra Araújo ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6320 ; 6381 ; 6071
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    A 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
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    Segmentation of COVID-19 Lesions in CT Images
    ( 2021) Joana Maria Rocha ; Sofia Cardoso Pereira ; Aurélio Campilho ; Ana Maria Mendonça ; 6071 ; 6381 ; 7800 ; 8251
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    Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography
    ( 2018) Guilherme Moreira Aresta ; Teresa Finisterra Araújo ; Jacobs,C ; Ginneken,Bv ; António Cunha ; Ramos,I ; Aurélio Campilho ; 6320 ; 6071 ; 6271 ; 6321
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    UOLO - Automatic Object Detection and Segmentation in Biomedical Images
    ( 2018) Teresa Finisterra Araújo ; Guilherme Moreira Aresta ; Adrian Galdran ; Costa,P ; Ana Maria Mendonça ; Aurélio Campilho ; 6825 ; 6321 ; 6320 ; 6381 ; 6071