Teresa Araújo
Teresa Araújo
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Classification of breast cancer histology images using convolutional neural networks
T Araújo, G Aresta, E Castro, J Rouco, P Aguiar, C Eloy, A Polónia, ...
PloS one 12 (6), e0177544, 2017
Bach: Grand challenge on breast cancer histology images
G Aresta, T Araújo, S Kwok, SS Chennamsetty, M Safwan, V Alex, ...
Medical image analysis 56, 122-139, 2019
Idrid: Diabetic retinopathy–segmentation and grading challenge
P Porwal, S Pachade, M Kokare, G Deshmukh, J Son, W Bae, L Liu, ...
Medical image analysis 59, 101561, 2020
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery
H Al Hajj, M Lamard, PH Conze, S Roychowdhury, X Hu, G Maršalkaitė, ...
Medical image analysis 52, 24-41, 2019
Data-driven color augmentation techniques for deep skin image analysis
A Galdran, A Alvarez-Gila, MI Meyer, CL Saratxaga, T Araújo, E Garrote, ...
arXiv preprint arXiv:1703.03702, 2017
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
G Aresta, C Jacobs, T Araújo, A Cunha, I Ramos, B van Ginneken, ...
Scientific reports 9 (1), 1-9, 2019
DR| GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
T Araújo, G Aresta, L Mendonça, S Penas, C Maia, Â Carneiro, ...
Medical Image Analysis 63, 101715, 2020
EyeWes: weakly supervised pre-trained convolutional neural networks for diabetic retinopathy detection
P Costa, T Araújo, G Aresta, A Galdran, AM Mendonça, A Smailagic, ...
2019 16th international conference on machine vision applications (MVA), 1-6, 2019
UOLO-automatic object detection and segmentation in biomedical images
T Araújo, G Aresta, A Galdran, P Costa, AM Mendonça, A Campilho
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018
A no-reference quality metric for retinal vessel tree segmentation
A Galdran, P Costa, A Bria, T Araújo, AM Mendonça, A Campilho
International Conference on Medical Image Computing and Computer-Assisted …, 2018
Towards an automatic lung cancer screening system in low dose computed tomography
G Aresta, T Araújo, C Jacobs, B Ginneken, A Cunha, I Ramos, ...
Image Analysis for Moving Organ, Breast, and Thoracic Images, 310-318, 2018
Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images
T Araújo, G Aresta, L Mendonça, S Penas, C Maia, Â Carneiro, ...
IEEE Access 8, 182462-182474, 2020
Automatic lung nodule detection combined with gaze information improves radiologists’ screening performance
G Aresta, C Ferreira, J Pedrosa, T Araújo, J Rebelo, E Negrão, ...
IEEE journal of biomedical and health informatics 24 (10), 2894-2901, 2020
Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images
T Araújo, AM Mendonça, A Campilho
PloS one 13 (4), e0194702, 2018
Segmentation and three‐dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)
T Araújo, M Abayazid, MJCM Rutten, S Misra
The International Journal of Medical Robotics and Computer Assisted Surgery …, 2017
Artificial intelligence improves the accuracy in histologic classification of breast lesions
A Polónia, S Campelos, A Ribeiro, I Aymore, D Pinto, M Biskup-Fruzynska, ...
American Journal of Clinical Pathology 155 (4), 527-536, 2021
Analysis of the performance of specialists and an automatic algorithm in retinal image quality assessment
DS Wanderley, T Araújo, CB Carvalho, C Maia, S Penas, Â Carneiro, ...
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), 1-4, 2019
Retinal image quality assessment by mean-subtracted contrast-normalized coefficients
A Galdran, T Araújo, AM Mendonça, A Campilho
European Congress on Computational Methods in Applied Sciences and …, 2017
Improving convolutional neural network design via variable neighborhood search
T Araújo, G Aresta, B Almada-Lobo, AM Mendonça, A Campilho
International Conference Image Analysis and Recognition, 371-379, 2017
Estimation of retinal vessel caliber using model fitting and random forests
T Araújo, AM Mendonça, A Campilho
Medical Imaging 2017: Computer-Aided Diagnosis 10134, 394-401, 2017
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