Ana Maria Rodrigues de Sousa Faria Mendonca
Verified @fe.up.pt
Scopus Publications
- Multitask Learning Approach for Foveal Avascular Zone Segmentation in OCTA Images
Tânia Melo, Ângela Carneiro, Aurélio Campilho, Ana Maria Mendonça
Lecture Notes in Computer Science, 2026 - Grad-CAM: The impact of large receptive fields and other caveats
Rui Santos, João Pedrosa, Ana Maria Mendonça, Aurélio Campilho
Computer Vision and Image Understanding, 2025 - Anatomically-Guided Inpainting for Local Synthesis of Normal Chest Radiographs
João Pedrosa, Sofia Cardoso Pereira, Joana Silva, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025 - CLARE-XR: explainable regression-based classification of chest radiographs with label embeddings
Joana Rocha, Sofia Cardoso Pereira, Pedro Sousa, Aurélio Campilho, Ana Maria Mendonça
Scientific Reports, 2024
An automatic system for pathology classification in chest X-ray scans needs more than predictive performance, since providing explanations is deemed essential for fostering end-user trust, improving decision-making, and regulatory compliance. CLARE-XR is a novel methodology that, when presented with an X-ray image, identifies the associated pathologies and provides explanations based on the presentation of similar cases. The diagnosis is achieved using a regression model that maps an image into a 2D latent space containing the reference coordinates of all findings. The references are generated once through label embedding, before the regression step, by converting the original binary ground-truth annotations to 2D coordinates. The classification is inferred minding the distance from the coordinates of an inference image to the reference coordinates. Furthermore, as the regressor is trained on a known set of images, the distance from the coordinates of an inference image to the coordinates of the training set images also allows retrieving similar instances, mimicking the common clinical practice of comparing scans to confirm diagnoses. This inherently interpretable framework discloses specific classification rules and visual explanations through automatic image retrieval methods, outperforming the multi-label ResNet50 classification baseline across multiple evaluation settings on the NIH ChestX-ray14 dataset. - Distribution-based detection of radiographic changes in pneumonia patterns: A COVID-19 case study
Sofia C. Pereira, Joana Rocha, Aurélio Campilho, Ana Maria Mendonça
Heliyon, 2024
Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising; however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns. Using a population-based approach, our approach utilizes distributional anomaly detection. This method diverges from traditional instance-wise approaches by focusing on sets of scans instead of individual images. Using an autoencoder to extract feature representations, we present instance-based and distribution-based assessments of the separability between COVID-positive and COVID-negative pneumonia radiographs. The results demonstrate that the proposed distribution-based methodology outperforms conventional instance-based techniques in identifying radiographic changes associated with COVID-positive cases. This underscores its potential as an early warning system capable of detecting significant distributional shifts in radiographic data. By continuously monitoring these changes, this approach offers a mechanism for early identification of emerging health trends, potentially signaling the onset of new pandemics and enabling prompt public health responses. - Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration
Mariana Miranda, Joana Santos-Oliveira, Ana Maria Mendonça, Vânia Sousa, Tânia Melo, Ângela Carneiro
Diagnostics, 2024
Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches. - Automated image label extraction from radiology reports — A review
Sofia C. Pereira, Ana Maria Mendonça, Aurélio Campilho, Pedro Sousa, Carla Teixeira Lopes
Artificial Intelligence in Medicine, 2024
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones. - Evaluating Visual Explainability in Chest X-Ray Pathology Detection
Pedro Pereira, Joana Rocha, João Pedrosa, Ana Maria Mendonça
2024 IEEE 22nd Mediterranean Electrotechnical Conference MELECON 2024, 2024
Chest X-Ray (CXR), plays a vital role in diagnosing lung and heart conditions, but the high demand for CXR examinations poses challenges for radiologists. Automatic support systems can ease this burden by assisting radiologists in the image analysis process. While Deep Learning models have shown promise in this task, concerns persist regarding their complexity and decision-making opacity. To address this, various visual explanation techniques have been developed to elucidate the model reasoning, some of which have received significant attention in literature and are widely used such as GradCAM. However, it is unclear how different explanations methods perform and how to quantitatively measure their performance, as well as how that performance may be dependent on the model architecture used and the dataset characteristics. In this work, two widely used deep classification networks - DenseNet121 and ResNet50 - are trained for multi-pathology classification on CXR and visual explanations are then generated using GradCAM, GradCAM++, EigenGrad-CAM, Saliency maps, LRP and DeepLift. These explanations methods are then compared with radiologist annotations using previously proposed explainability evaluations metrics - intersection over union and hit rate. Furthermore, a novel method to convey visual explanations in the form of radiological written reports is proposed, allowing for a clinically-oriented explainability evaluation metric - zones score. It is shown that Grad-CAM++ and Saliency methods offer the most accurate explanations and that the effectiveness of visual explanations is found to vary based on the model and corresponding input size. Additionally, the explainability performance across different CXR datasets is evaluated, highlighting that the explanation quality depends on the dataset’s characteristics and annotations. - STERN: Attention-driven Spatial Transformer Network for abnormality detection in chest X-ray images
Joana Rocha, Sofia Cardoso Pereira, João Pedrosa, Aurélio Campilho, Ana Maria Mendonça
Artificial Intelligence in Medicine, 2024 - DeepClean - Contrastive Learning Towards Quality Assessment in Large-Scale CXR Data Sets
Sofia C. Pereira, João Pedrosa, Joana Rocha, Pedro Sousa, Aurélio Campilho, Ana Maria Mendonça
Proceedings 2024 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2024, 2024
Large-scale datasets are essential for training deep learning models in medical imaging. However, many of these datasets contain poor-quality images that can compromise model performance and clinical reliability. In this study, we propose a framework to detect non-compliant images, such as corrupted scans, incomplete thorax X-rays, and images of non-thoracic body parts, by leveraging contrastive learning for feature extraction and parametric or non-parametric scoring methods for out-of-distribution ranking. Our approach was developed and tested on the CheXpert dataset, achieving an AUC of 0.75 in a manually labeled subset of 1,000 images, and further qualitatively and visually validated on the external PadChest dataset, where it also performed effectively. Our results demonstrate the potential of contrastive learning to detect non-compliant images in large-scale medical datasets, laying the foundation for future work on reducing dataset pollution and improving the robustness of deep learning models in clinical practice. - Lightweight multi-scale classification of chest radiographs via size-specific batch normalization
Sofia C． Pereira, Joana Rocha, Aurélio Campilho, Pedro Sousa, Ana Maria Mendonça
Computer Methods and Programs in Biomedicine, 2023 - Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework
Tânia Melo, Ângela Carneiro, Aurélio Campilho, Ana Maria Mendonça
Journal of Medical Imaging, 2023 - Confident-CAM: Improving Heat Map Interpretation in Chest X-Ray Image Classification
Joana Rocha, Sofia Cardoso Pereira, Aurélio Campilho, Ana Maria Mendonça
Proceedings 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2023, 2023 - An Active Learning Approach for Support Device Detection in Chest Radiography Images
Raquel Belo, Joana Rocha, Ana Maria Mendonça, Aurélio Campilho
Proceedings of SPIE the International Society for Optical Engineering, 2023 - Automatic Eye-Tracking-Assisted Chest Radiography Pathology Screening
Rui Santos, João Pedrosa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023 - Semi-supervised Multi-structure Segmentation in Chest X-Ray Imaging
Ricardo Coimbra Brioso, João Pedrosa, Ana Maria Mendonça, Aurélio Campilho
Proceedings IEEE Symposium on Computer Based Medical Systems, 2023 - OCT Image Synthesis through Deep Generative Models
Tânia Melo, Jaime Cardoso, Ângela Carneiro, Aurélio Campilho, Ana Maria Mendonça
Proceedings IEEE Symposium on Computer Based Medical Systems, 2023 - Deep Feature-Based Automated Chest Radiography Compliance Assessment
Matilde Costa, Sofia C. Pereira, João Pedrosa, Ana Maria Mendonça, Aurélio Campilho
2023 IEEE 7th Portuguese Meeting on Bioengineering Enbeng 2023, 2023 - Lesion-Aware Chest Radiography Abnormality Classification with Object Detection Framework
João Pedrosa, Pedro Sousa, Joana Silva, Ana Maria Mendonça, Aurélio Campilho
Proceedings IEEE Symposium on Computer Based Medical Systems, 2023 - Addressing Chest Radiograph Projection Bias in Deep Classification Models
Proceedings of Machine Learning Research, 2023 - Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning
João Pedrosa, Guilherme Aresta, Carlos Ferreira, Catarina Carvalho, Joana Silva, Pedro Sousa, Lucas Ribeiro, Ana Maria Mendonça, Aurélio Campilho
Scientific Reports, 2022 - Retinal and choroidal vasoreactivity in central serous chorioretinopathy
Susana Penas, Teresa Araújo, Ana Maria Mendonça, Simão Faria, Jorge Silva, Aurélio Campilho, Maria Lurdes Martins, Vânia Sousa, Amândio Rocha-Sousa, Ângela Carneiro, Fernando Falcão-Reis
Graefe S Archive for Clinical and Experimental Ophthalmology, 2022 - Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images
Joana Rocha, Sofia Cardoso Pereira, Joao Pedrosa, Aurelio Campilho, Ana Maria Mendonca
Proceedings IEEE Symposium on Computer Based Medical Systems, 2022 - Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection
João Pedrosa, Pedro Sousa, Joana Silva, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022 - A Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification
Joana Rocha, Ana Maria Mendonça, Aurélio Campilho
U Porto Journal of Engineering, 2021 - Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation
Beatriz Remeseiro, Ana Maria Mendonça, Aurélio Campilho
Visual Computer, 2021 - Segmentation of covid-19 lesions in CT images
Joana Rocha, Sofia Pereira, Aurelio Campilho, Ana Maria Mendonca
Bhi 2021 2021 IEEE EMBS International Conference on Biomedical and Health Informatics Proceedings, 2021 - Chest Radiography Few-Shot Image Synthesis for Automated Pathology Screening Applications
Martim Quintas E Sousa, Joao Pedrosa, Joana Rocha, Sofia Cardoso Pereira, Ana Maria Mendonca, Aurelio Campilho
Proceedings 2021 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2021, 2021 - Microaneurysm detection in color eye fundus images for diabetic retinopathy screening
Tânia Melo, Ana Maria Mendonça, Aurélio Campilho
Computers in Biology and Medicine, 2020 - DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
Teresa Araújo, Guilherme Aresta, Luís Mendonça, Susana Penas, Carolina Maia, Ângela Carneiro, Ana Maria Mendonça, Aurélio Campilho
Medical Image Analysis, 2020 - Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images
Joana Rocha, António Cunha, Ana Maria Mendonça
Journal of Medical Systems, 2020 - Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter
Joana Rocha, António Cunha, Ana Maria Mendonça
Ifmbe Proceedings, 2020 - Optic disc and fovea detection in color eye fundus images
Ana Maria Mendonça, Tânia Melo, Teresa Araújo, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020 - A multi-dataset approach for dme risk detection in eye fundus images
Catarina Carvalho, João Pedrosa, Carolina Maia, Susana Penas, Ângela Carneiro, Luís Mendonça, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020 - Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images
Teresa Araujo, Guilherme Aresta, Luis Mendonca, Susana Penas, Carolina Maia, Angela Carneiro, Ana Maria Mendonca, Aurelio Campilho
IEEE Access, 2020 - IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, Jianzong Wang, Xinhui Liu, Liangxin Gao, TianBo Wu, Jing Xiao, Fengyan Wang, Baocai Yin, Yunzhi Wang, Gopichandh Danala, Linsheng He, Yoon Ho Choi, Yeong Chan Lee, Sang-Hyuk Jung, Zhongyu Li, Xiaodan Sui, Junyan Wu, Xiaolong Li, Ting Zhou, Janos Toth, Agnes Baran, Avinash Kori, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Xingzheng Lyu, Li Cheng, Qinhao Chu, Pengcheng Li, Xin Ji, Sanyuan Zhang, Yaxin Shen, Ling Dai, Oindrila Saha, Rachana Sathish, Tânia Melo, Teresa Araújo, Balazs Harangi, Bin Sheng, Ruogu Fang, Debdoot Sheet, Andras Hajdu, Yuanjie Zheng, Ana Maria Mendonça, Shaoting Zhang, Aurélio Campilho, Bin Zheng, Dinggang Shen, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau
Medical Image Analysis, 2020 - EyeWeS: Weakly supervised pre-trained convolutional neural networks for diabetic retinopathy detection
Pedro Costa, Teresa Araujo, Guilherme Aresta, Adrian Galdran, Ana Maria Mendonca, Asim Smailagic, Aurelio Campilho
Proceedings of the 16th International Conference on Machine Vision Applications Mva 2019, 2019 - Quantitative assessment of central serous chorioretinopathy in angiographic sequences of retinal images
Carlos A. Ferreira, Susana Penas, Jorge Silva, Ana Maria Mendonca
6th IEEE Portuguese Meeting on Bioengineering Enbeng 2019 Proceedings, 2019 - Wide residual network for Lung-Rads™ screening referral
Carlos A. Ferreira, Guilherme Aresta, Antonio Cunha, Ana Maria Mendonca, Aurelio Campilho
6th IEEE Portuguese Meeting on Bioengineering Enbeng 2019 Proceedings, 2019 - Analysis of the performance of specialists and an automatic algorithm in retinal image quality assessment
Diego S. Wanderley, Teresa Araujo, Catarina B. Carvalho, Carolina Maia, Susana Penas, Angela Carneiro, Ana Maria Mendonca, Aurelio Campilho
6th IEEE Portuguese Meeting on Bioengineering Enbeng 2019 Proceedings, 2019 - Uncertainty-aware artery/vein classification on retinal images
Adrian Galdran, M. Meyer, P. Costa, MendonCa, A. Campilho
Proceedings International Symposium on Biomedical Imaging, 2019 - An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans
Elham Shakibapour, António Cunha, Guilherme Aresta, Ana Maria Mendonça, Aurélio Campilho
Expert Systems with Applications, 2019 - Comparison of conventional and deep learning based methods for pulmonary nodule segmentation in CT images
Joana Rocha, António Cunha, Ana Maria Mendonça
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019 - Convolutional neural network architectures for texture classification of pulmonary nodules
Carlos A. Ferreira, António Cunha, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019 - Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images
Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho
Plos One, 2018 - End-to-End Adversarial Retinal Image Synthesis
Pedro Costa, Adrian Galdran, Maria Ines Meyer, Meindert Niemeijer, Michael Abramoff, Ana Maria Mendonca, Aurelio Campilho
IEEE Transactions on Medical Imaging, 2018 - Creation of Retinal Mosaics for Diabetic Retinopathy Screening: A Comparative Study
Tânia Melo, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - Retinal image quality assessment by mean-subtracted contrast-normalized coefficients
Adrian Galdran, Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computational Vision and Biomechanics, 2018 - A pixel-wise distance regression approach for joint retinal optical disc and fovea detection
Maria Ines Meyer, Adrian Galdran, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - 3D mapping of choroidal thickness from OCT B-scans
Simão P. Faria, Susana Penas, Luís Mendonça, Jorge A. Silva, Ana Maria Mendonça
Lecture Notes in Computational Vision and Biomechanics, 2018 - Automatic Characterization of the serous Retinal Detachment Associated with the subretinal Fluid Presence in Optical Coherence Tomography Images
Joaquim de Moura, Jorge Novo, Susana Penas, Marcos Ortega, Jorge Silva, Ana Maria Mendonça
Procedia Computer Science, 2018 - A no-reference quality metric for retinal vessel tree segmentation
Adrian Galdran, Pedro Costa, Alessandro Bria, Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - Uolo - Automatic object detection and segmentation in biomedical images
Teresa Araújo, Guilherme Aresta, Adrian Galdran, Pedro Costa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018 - Objective quality assessment of retinal images based on texture features
Beatriz Remeseiro, Ana Maria Mendonca, Aurelio Campilho
Proceedings of the International Joint Conference on Neural Networks, 2017 - Estimation of retinal vessel caliber using model fitting and random forests
Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2017 - Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs
Ana Maria Mendonça, Beatriz Remeseiro, Behdad Dashtbozorg, Aurélio Campilho
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2017 - Adversarial synthesis of retinal images from vessel trees
Pedro Costa, Adrian Galdran, Maria Ines Meyer, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017 - A deep neural network for vessel segmentation of Scanning Laser Ophthalmoscopy images
Maria Ines Meyer, Pedro Costa, Adrian Galdran, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017 - Improving convolutional neural network design via variable neighborhood search
Teresa Araújo, Guilherme Aresta, Bernardo Almada-Lobo, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017 - 3D lung nodule candidate detection in multiple scales
Jorge Novo, Luis Goncalves, Ana Maria Mendonca, Aurelio Campilho
Proceedings of the 14th Iapr International Conference on Machine Vision Applications Mva 2015, 2015 - Assessment of retinal vascular changes through arteriolar-to-venular ratio calculation
Behdad Dashtbozorg, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015 - Optic disc segmentation using the sliding band filter
Behdad Dashtbozorg, Ana Maria Mendonça, Aurélio Campilho
Computers in Biology and Medicine, 2015 - RetinaCAD, a system for the assessment of retinal vascular changes
Behdad Dashtbozorg, Ana Maria Mendonca, Susana Penas, Aurelio Campilho
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Embc 2014, 2014 - An automatic graph-based approach for artery/Vein classification in retinal images
Behdad Dashtbozorg, Ana Maria Mendonca, Aurelio Campilho
IEEE Transactions on Image Processing, 2014 - Segmentation of the vascular network of the Retina
E. Y. K. Ng, U. Rajendra Acharya, Jasjit S. Suri, Aurélio Campilho
Image Analysis and Modeling in Ophthalmology, 2014 - Assessment of vascular changes in retinal images
Behdad Dashtbozorg, Ana Maria Mendonca, Aurelio Campilho
IEEE Memea 2014 IEEE International Symposium on Medical Measurements and Applications Proceedings, 2014 - Reliable lung segmentation methodology by including juxtapleural nodules
J. Novo, J. Rouco, A. Mendonça, Aurélio Campilho
Lecture Notes in Computer Science, 2014 - An automatic method for the estimation of Arteriolar-to-Venular Ratio in retinal images
Behdad Dashtbozorg, Ana Maria Mendonca, Aurelio Campilho
Proceedings of CBMS 2013 26th IEEE International Symposium on Computer Based Medical Systems, 2013 - Automatic classification of retinal vessels using structural and intensity information
Behdad Dashtbozorg, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013 - Automatic localization of the optic disc by combining vascular and intensity information
Ana Maria Mendonça, António Sousa, Luís Mendonça, Aurélio Campilho
Computerized Medical Imaging and Graphics, 2013 - E-cadherin and adherens-junctions stability in gastric carcinoma: Functional implications of glycosyltransferases involving N-glycan branching biosynthesis, N-acetylglucosaminyltransferases III and v
Salomé S. Pinho, Joana Figueiredo, Joana Cabral, Sandra Carvalho, Joana Dourado, Ana Magalhães, Fátima Gärtner, Ana Maria Mendonça, Tomoya Isaji, Jianguo Gu, Fátima Carneiro, Raquel Seruca, Naoyuki Taniguchi, Celso A. Reis
Biochimica Et Biophysica Acta General Subjects, 2013 - Automatic estimation of the arteriolar-to-venular ratio in retinal images using a graph-based approach for artery/vein classification
Behdad Dashtbozorg, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013 - An automatic method for the estimation of Arteriolar-to-Venular Ratio in retinal images
Proceedings IEEE Symposium on Computer Based Medical Systems, 2013 - Automatic lane segmentation in TLC images using the continuous wavelet transform
Bruno Moreira, António Sousa, Ana Maria Mendonça, Aurélio Campilho
Computational and Mathematical Methods in Medicine, 2013 - Lane background removal for the classification of thin-layer chromatography images
Bruno M. Moreira, António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013 - Correction of geometrical distortions in bands of chromatography images
Bruno M. Moreira, António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013 - Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection
Tiago Esteves, Pedro Quelhas, Ana Maria Mendonça, Aurélio Campilho
Machine Vision and Applications, 2012 - Automatic localization of the optic disc in retinal images based on the entropy of vascular directions
Ana Maria Mendonça, Filipe Cardoso, António V. Sousa, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012 - Automatic lane detection in chromatography images
Bruno M. Moreira, António V. Sousa, Ana M. Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012 - Automatic segmentation of chromatographic images for region of interest delineation
Ana M. Mendonça, António V. Sousa, M. Clara Sá-Miranda, Aurélio C. Campilho
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2011 - Classification-based segmentation of the region of interest in chromatographic images
António V. Sousa, Ana Maria Mendonc̨a, M. Clara Sá-Miranda, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011 - Arabidopsis thaliana automatic cell file detection and cell length estimation
Pedro Quelhas, Jeroen Nieuwland, Walter Dewitte, Ana Maria Mendonça, Jim Murray, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011 - Cell nuclei and cytoplasm joint segmentation using the sliding band filter
Pedro Quelhas, Monica Marcuzzo, Ana Maria Mendonca, Aurélio Campilho
IEEE Transactions on Medical Imaging, 2010 - 3D cell nuclei fluorescence quantification using sliding band filter
Pedro Quelhas, Ana Maria Mendonca, Aurelio Campilho
Proceedings International Conference on Pattern Recognition, 2010 - Optical flow based Arabidopsis thaliana root meristem cell division detection
Pedro Quelhas, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010 - Fibronectin-mediated endothelialisation of chitosan porous matrices
Isabel F. Amaral, Ronald E. Unger, Sabine Fuchs, Ana M. Mendonça, Susana R. Sousa, Mário A. Barbosa, Ana P. Pêgo, C.J. Kirkpatrick
Biomaterials, 2009 - Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging
Monica Marcuzzo, Pedro Quelhas, Ana Campilho, Ana Maria Mendonça, Aurélio Campilho
Computers in Biology and Medicine, 2009 - Chromatographic pattern recognition using optimized one-class classifiers
António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009 - Cell division detection on the arabidopsis thaliana root
Monica Marcuzzo, Tiago Guichard, Pedro Quelhas, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009 - Evaluation of symmetry enhanced sliding band filter for plant cell nuclei detection in low contrast noisy fluorescent images
Monica Marcuzzo, Pedro Quelhas, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009 - Cancer cell detection and invasion depth estimation in brightfield images
Pedro Quelhas, Monica Marcuzzo, Ana Maria Mendonça, Maria José Oliveira, Aurelio Campilho
British Machine Vision Conference Bmvc 2009 Proceedings, 2009 - Dissimilarity-based classification of chromatographic profiles
António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Pattern Analysis and Applications, 2008 - Chromatographic pattern classification
AntÓnio V. Sousa, Ana Maria MendonÇa, AurÉlio Campilho
IEEE Transactions on Biomedical Engineering, 2008 - Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers
António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008 - Tracking of Arabidopsis thaliana root cells in time-lapse microscopy
Monica Marcuzzo, Pedro Quelhas, Ana Maria Mendonca, Aurelio Campilho
Proceedings International Conference on Pattern Recognition, 2008 - Automatic cell segmentation from confocal microscopy images of the Arabidopsis root
Monica Marcuzzo, Pedro Quelhas, Ana Campilho, Ana Maria Mendonca, Aurelio Campilho
2008 5th IEEE International Symposium on Biomedical Imaging from Nano to Macro Proceedings Isbi, 2008 - A hybrid approach for arabidopsis root cell image segmentation
Monica Marcuzzo, Pedro Quelhas, Ana Campilho, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008 - Evaluation of contrast enhancement filters for lung nodule detection
Carlos S. Pereira, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007 - Detection of lung nodule candidates in chest radiographs
Carlos S. Pereira, Hugo Fernandes, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007 - Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction
A.M. Mendonca, A. Campilho
IEEE Transactions on Medical Imaging, 2006 - The class imbalance problem in TLC image classification
António V. Sousa, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006 - A multiclassifier approach for lung nodule classification
Carlos S. Pereira, Luís A. Alexandre, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006 - Feature extraction for classification of thin-layer chromatography images
António V. Sousa, Ana Maria Mendonça, Aurélio Campilho, Rui Aguiar, C. Sá Miranda
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2005 - Detection of Rib Borders on X-ray Chest Radiographs
Rui Moreira, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2004 - Automatic delimitation of lung fields on chest radiographs
A.M. Mendonca, J.A. da Silva, A. Campilho
2004 2nd IEEE International Symposium on Biomedical Imaging Macro to Nano, 2004 - Automatic lane and band detection in images of thin layer chromatography
António V. Sousa, Rui Aguiar, Ana Maria Mendonça, Aurélio Campilho
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2004 - Detection and validation of lung field contours on chest radiographs
Proceedings of the IASTED International Conference on Biomedical Engineering, 2003 - A neural network approach for the automatic detection of microaneurysms in retinal angiograms
Proceedings of the International Joint Conference on Neural Networks, 2001 - Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients
A.M. Mendonca, A.J. Campilho, J.M. Nunes
Proceedings International Conference on Image Analysis and Processing Iciap 1999, 1999 - A new similarity criterion for retinal image registration
A.M. Mendonca, A. Campilho, J.M.R. Nunes
Proceedings International Conference on Image Processing Icip, 1994
