J
José Ignacio Orlando
Researcher at Medical University of Vienna
Publications - 53
Citations - 1870
José Ignacio Orlando is an academic researcher from Medical University of Vienna. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 13, co-authored 41 publications receiving 1120 citations. Previous affiliations of José Ignacio Orlando include National University of Central Buenos Aires & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
TL;DR: Results suggest that this method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
Journal ArticleDOI
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando,Huazhu Fu,João Barbossa Breda,Karel Van Keer,Deepti R. Bathula,Andres Diaz-Pinto,Ruogu Fang,Pheng-Ann Heng,Jeyoung Kim,Joon-Ho 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,Xiulan Zhang,Yanwu Xu,Hrvoje Bogunovic +30 more
TL;DR: It is observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task, and the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.
Journal ArticleDOI
An ensemble deep learning based approach for red lesion detection in fundus images.
TL;DR: This paper proposes a novel method for red lesion detection based on combining both deep learned and domain knowledge that reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert.
Book ChapterDOI
Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images
TL;DR: This work presents a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model with more expressive potentials, and employs recent results enabling extremely fast inference in a fully connected model.
Proceedings ArticleDOI
Convolutional neural network transfer for automated glaucoma identification
TL;DR: Results on the Drishti-GS1 dataset suggests the viability of using CNNs that are pre-trained from non-medical data for automated glaucoma detection and offer significant evidence of the importance of well-chosen image pre-processing for transfer learning when the amount of data is not sufficient for fine-tuning the network.