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de interesse
Detalhes

Detalhes

006
Publicações

2020

Automatic lung nodule detection combined with gaze information improves radiologists' screening performance

Autores
Aresta, G; Ramos, I; Campilho, A; Ferreira, C; Pedrosa, J; Araujo, T; Rebelo, J; Negrao, E; Morgado, M; Alves, F; Cunha, A;

Publicação
IEEE Journal of Biomedical and Health Informatics

Abstract

2020

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Autores
Porwal, P; Pachade, S; Kokare, M; Deshmukh, G; Son, J; Bae, W; Liu, LH; Wang, J; Liu, XH; Gao, LX; Wu, TB; Xiao, J; Wang, FY; Yin, BC; Wang, YZ; Danala, G; He, LS; Choi, YH; Lee, YC; Jung, SH; Li, ZY; Sui, XD; Wu, JY; Li, XL; Zhou, T; Toth, J; Bara, A; Kori, A; Chennamsetty, SS; Safwan, M; Alex, V; Lyu, XZ; Cheng, L; Chu, QH; Li, PC; Ji, X; Zhang, SY; Shen, YX; Dai, L; Saha, O; Sathish, R; Melo, T; Araujo, T; Harangi, B; Sheng, B; Fang, RG; Sheet, D; Hajdu, A; Zheng, YJ; Mendonca, AM; Zhang, ST; Campilho, A; Zheng, B; Shen, D; Giancardo, L; Quellec, G; Meriaudeau, F;

Publicação
Medical Image Analysis

Abstract

2020

DR|GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images

Autores
Araújo, T; Aresta, G; Mendonça, L; Penas, S; Maia, C; Carneiro, A; Mendonça, AM; Campilho, A;

Publicação
Medical Image Analysis

Abstract

2020

O-MedAL: Online active deep learning for medical image analysis

Autores
Smailagic, A; Costa, P; Gaudio, A; Khandelwal, K; Mirshekari, M; Fagert, J; Walawalkar, D; Xu, S; Galdran, A; Zhang, P; Campilho, A; Noh, HY;

Publicação
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under:. Technologies > Machine Learning. Technologies > Classification. Application Areas > Health Care. © 2020 Wiley Periodicals, Inc.

2020

Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation

Autores
Remeseiro, B; Mendonça, AM; Campilho, A;

Publicação
The Visual Computer

Abstract

Teses
supervisionadas

2019

content based image retrieval as a computer aided diagnosis tool for radiologists

Autor
José Ricardo Ferreira de Castro Ramos

Instituição
UP-FEUP

2019

Diabetic Retinopathy Grading in Color Eye Fundus Images

Autor
Teresa Manuel Sá Finisterra Araújo

Instituição
UP-FEUP

2019

Ovarian structures segmentation using a neural network approach

Autor
Sónia Alexandra Cardoso Marques

Instituição
UP-FEUP

2019

Lung nodule characterization and follow-up assessment

Autor
Daniela Marisa da Silva Campos

Instituição
UP-FEUP

2019

Segmentation and Quantification of Gynecological Structures from Ultrasound Images

Autor
Diego Santos Wanderley

Instituição
UP-FEUP