Deep Learning | José Ignacio Orlando

SketchZooms: Deep multi-view descriptors for matching line drawings

We develop a deep neural network that automatically extracts contextual features from patches in sketches, trained with 3D models rendered with non-photorealistic techniques. Our method is able to find dense correspondences between real world sketches!

Pablo Navarro, José Ignacio Orlando, Claudio Delrieux, Emmanuel Iarussi

SketchZooms: Deep multi-view descriptors for matching line drawings

Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets

We developed a deep learning based pipeline to segment the lumen boundary in IVUS datasets based on multiframes inputs.

Paulo GP Ziemer, Carlos a Bulant, José Ignacio Orlando, Gonzalo D Maso Talou, Luis a Mansilla Álvarez, Cristiano Guedes Bezerra, Pedro a Lemos, Héctor M García-García, Pablo J Blanco

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

This is the summary publication of the AGE challenge on angle closure glaucoma detection from OCT scans of the anterior segment.

Huazhu Fu, Fei Li, Xu Sun, Xingxing Cao, Jingan Liao, José Ignacio Orlando, Xing Tao, Yuexiang Li, Shihao Zhang, Mingkui Tan, Chenglang Yuan, Cheng Bian, Ruitao Xie, Jiongcheng Li, Xiaomeng Li, Jing Wang, Le Geng, Panming Li, Huaying Hao, Jiang Liu, Yan Kong, Yongyong Ren, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

We introduced a fully automated approach to segment the photorceptor layer, evaluate its thickness and track potential disruptions using an ensemble of deep neural networks.

José Ignacio Orlando, Bianca S Gerendas, Sophie Riedl, Christoph Grechenig, Anna Breger, Martin Ehler, Sebastian M Waldstein, Hrvoje Bogunović, Ursula Schmidt-Erfurth

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina

We applied CycleGANs to reduce the covariate shift of models trained on one OCT vendor and evaluated on a different one. And it works quite well!

David Romo-Bucheli, Philipp Seeböck, José Ignacio Orlando, Bianca S. Gerendas, Sebastian M. Waldstein, U Schmidt-Erfurth, Hrvoje Bogunović

On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems

Anna introduced a new mathematical approach for dimensionality reduction that we incorporated into loss functions to augment target information and improve performance.

Anna Breger, José Ignacio Orlando, Pavol Harar, Monika Dörfler, Sophie Klimscha, Christoph Grechenig, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler

Multiclass Segmentation as Multitask Learning for Drusen Segmentation in Retinal Optical Coherence Tomography

We posed a multiclass segmentation task as a single multitask model with binary segmentation targets. Our results indicate that this approach might be useful to deal with “sandwiched” structures.

Rhona Asgari, José Ignacio Orlando, Sebastian M. Waldstein, Ferdinand Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunović

An Amplified-Target Loss Approach for Photoreceptor Layer Segmentation in Pathological OCT Scans

We introduce an augmented target loss function framework for photoreceptor layer segmentation that penalizes errors in the central area of each B-scan. It allows to significantly improve performance with respect to the standard loss functions.

José Ignacio Orlando, Anna Breger, Hrvoje Bogunović, Sophie Riedl, Bianca S. Gerendas, Martin Ehler, Ursula Schmidt-Erfurth

Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning

We introduced a multitask learning y-shaped neural network that simultaneously segment the FAZ in FA images and predict a distance map. This extra branch aids to improve results in clinical routine images.

Dominik Hofer, José Ignacio Orlando, Philipp Seeböck, Georgios Mylonas, Felix Goldbach, Amir Sadeghipour, Bianca S. Gerendas, Ursula Schmidt-Erfurth

REFUGE challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

This is the summary publication of the REFUGE challenge on glaucoma detection in color fundus photographs.

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