1 code implementation • 9 Jun 2017 • José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko
In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge.
1 code implementation • 13 May 2023 • Huihui Fang, Fei Li, Junde Wu, Huazhu Fu, Xu sun, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu
Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.
1 code implementation • 28 Sep 2022 • Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda, Francisco Filizzola, Nicolás Iannuzzo, Danila Nejamkin, Kevin Wignall, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando
In this paper we present a comprehensive analysis of different coarse-to-fine designs for OD/OC segmentation using 5 public databases, both from a standard segmentation perspective and for estimating the vCDR for glaucoma assessment.
1 code implementation • 6 Oct 2022 • Tomás Castilla, Marcela S. Martínez, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando
In this paper we propose to model a strong baseline for this task based on a simple and standard ResNet-18 architecture.
1 code implementation • 27 Jun 2023 • Duilio Deangeli, Emmanuel Iarussi, Juan Pablo Princich, Mariana Bendersky, Ignacio Larrabide, José Ignacio Orlando
This paper introduces a novel method to learn normal asymmetry patterns in homologous brain structures based on anomaly detection and representation learning.
no code implementations • 25 May 2018 • José Ignacio Orlando, João Barbosa Breda, Karel van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant
In this paper we propose a first approach for characterizing those changes using computational hemodynamics.
no code implementations • 25 Apr 2017 • José Ignacio Orlando, Hugo Luis Manterola, Enzo Ferrante, Federico Ariel
Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features.
no code implementations • 23 Jan 2019 • José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović, Sophie Klimscha, Christoph Grechenig, Sebastian Waldstein, Bianca S. Gerendas, Ursula Schmidt-Erfurth
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans.
Ranked #4 on Image Matting on AIM-500
no code implementations • 24 Jan 2019 • Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth
Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability.
no code implementations • 29 May 2019 • Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, Ursula Schmidt-Erfurth
We propose to take advantage of this property using bayesian deep learning, based on the assumption that epistemic uncertainties will correlate with anatomical deviations from a normal training set.
no code implementations • 18 Jun 2019 • Rhona Asgari, José Ignacio Orlando, Sebastian Waldstein, Ferdinand Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunović
We also introduce connections between each class-specific branch and the additional decoder to increase the regularization effect of this surrogate task.
no code implementations • 2 Aug 2019 • José Ignacio Orlando, Anna Breger, Hrvoje Bogunović, Sophie Riedl, Bianca S. Gerendas, Martin Ehler, Ursula Schmidt-Erfurth
Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions.
no code implementations • 8 Oct 2019 • José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti. R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, Joonho Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
no code implementations • 29 Nov 2019 • Pablo Navarro, José Ignacio Orlando, Claudio Delrieux, Emmanuel Iarussi
Finding point-wise correspondences between images is a long-standing problem in image analysis.
no code implementations • 14 Feb 2022 • Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
no code implementations • 16 Feb 2022 • Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
no code implementations • 18 Feb 2022 • Huihui Fang, Fei Li, Junde Wu, Huazhu Fu, Xu sun, Jaemin Son, Shuang Yu, Menglu Zhang, Chenglang Yuan, Cheng Bian, Baiying Lei, Benjian Zhao, Xinxing Xu, Shaohua Li, Francisco Fumero, José Sigut, Haidar Almubarak, Yakoub Bazi, Yuanhao Guo, Yating Zhou, Ujjwal Baid, Shubham Innani, Tianjiao Guo, Jie Yang, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
no code implementations • 5 Oct 2022 • Camila García, Yibin Fang, Jianmin Liu, Ana Paula Narata, José Ignacio Orlando, Ignacio Larrabide
While deep learning models have been applied for segmenting the brain vasculature in these images, they have never been used in cases with bAVMs.