Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

001
Publicações

2020

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

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

Publicação
IEEE Journal of Biomedical and Health Informatics

Abstract

2020

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

Autores
Araujo, T; Aresta, G; Mendonca, L; Penas, S; Maia, C; Carneiro, A; Maria Mendonca, AM; Campilho, A;

Publicação
Medical Image Analysis

Abstract

2020

Optic Disc and Fovea Detection in Color Eye Fundus Images

Autores
Mendonça, AM; Melo, T; Araújo, T; Campilho, A;

Publicação
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

Data Augmentation for Improving Proliferative Diabetic Retinopathy Detection in Eye Fundus Images

Autores
Araujo, T; Aresta, G; Mendonca, L; Penas, S; Maia, C; Carneiro, A; Mendonca, AM; Campilho, A;

Publicação
IEEE Access

Abstract

2019

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Autores
Al Hajj, H; Lamard, M; Conze, PH; Roychowdhury, S; Hu, XW; Marsalkaite, G; Zisimopoulos, O; Dedmari, MA; Zhao, FQ; Prellberg, J; Sahu, M; Galdran, A; Araujo, T; Vo, DM; Panda, C; Dahiya, N; Kondo, S; Bian, ZB; Vandat, A; Bialopetravicius, J; Flouty, E; Qiu, CH; Dill, S; Mukhopadhyay, A; Costa, P; Aresta, G; Ramamurthys, S; Lee, SW; Campilho, A; Zachow, S; Xia, SR; Conjeti, S; Stoyanov, D; Armaitis, J; Heng, PA; Macready, WG; Cochener, B; Quellec, G;

Publicação
Medical Image Analysis

Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. © 2018 Elsevier B.V.