C-BER - Indexed Articles in Conferences

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Browsing C-BER - Indexed Articles in Conferences by Author "6381"
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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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    Automatic Characterization of the serous Retinal Detachment Associated with the subretinal Fluid Presence in Optical Coherence Tomography Images
    ( 2018) Novo,J ; Silva,J ; Moura,JD ; Penas,S ; Ortega,M ; Ana Maria Mendonça ; 6381
    An accurate detection of the macular edema (ME) presence constitutes a crucial ophthalmological issue as it provides useful information for the identification, diagnosis and treatment of different relevant ocular and Systemic diseaseS. serous Retinal Detachment (sRD) is a particular type of ME, which is characterized by the leakage of fluid that has a propensity of being accumulated in the macular region. This paper proposes a new methodology for the automatic identification and characterization of the sRD edema using Optical Coherence Tomography (OCT) imageS. The subretinal fluids and the External Limiting Membrane (ELM) retinal layers are identified and characterized to measure the disease severity. Four different visualization modules were designed including representative derived parameters to facilitate the doctor's work in the diagnostic evaluation of ME. The different steps of this method were validated using the manual labelling provided by an expert clinician. The validation of the proposed method offered satisfactory results, constituting a suitable scenario with intuitive visual representations that also include different relevant biomarkerS. © 2018 The Author(s).
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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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    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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    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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    Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter
    ( 2020) Joana Maria Rocha ; António Cunha ; Ana Maria Mendonça ; 6271 ; 6381 ; 7800
    This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization. © 2020, Springer Nature Switzerland AG.
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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