Sebastian Otálora
Sebastian Otálora
Other namesJuan Sebastian Otálora Montenegro
Machine learning engineer,
Verified email at - Homepage
Cited by
Cited by
Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score
OJ del Toro, M Atzori, S Otálora, M Andersson, K Eurén, M Hedlund, ...
Medical Imaging 2017: Digital Pathology 10140, 165-173, 2017
Oct-net: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes
O Perdomo, S Otálora, FA González, F Meriaudeau, H Müller
2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018 …, 2018
Analysis of histopathology images: From traditional machine learning to deep learning
O Jimenez-del-Toro, S Otálora, M Andersson, K Eurén, M Hedlund, ...
Biomedical Texture Analysis, 281-314, 2017
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
O Perdomo, H Rios, FJ Rodríguez, S Otálora, F Meriaudeau, H Müller, ...
Computer methods and programs in biomedicine 178, 181-189, 2019
Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology
S Otálora, M Atzori, V Andrearczyk, A Khan, H Müller
Frontiers in bioengineering and biotechnology, 198, 2019
A novel machine learning model based on exudate localization to detect diabetic macular edema
O Perdomo, S Otalora, F Rodríguez, J Arevalo, FA González
Proceedings of the ophthalmic medical image analysis international workshop …, 2016
Training deep convolutional neural networks with active learning for exudate classification in eye fundus images
S Otálora, O Perdomo, F González, H Müller
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale …, 2017
Deep learning-based retrieval system for gigapixel histopathology cases and the open access literature
R Schaer, S Otálora, O Jimenez-del-Toro, M Atzori, H Müller
Journal of pathology informatics 10 (1), 19, 2019
Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification
N Marini, S Otálora, H Müller, M Atzori
Medical image analysis 73, 102165, 2021
Combining unsupervised feature learning and riesz wavelets for histopathology image representation: application to identifying anaplastic medulloblastoma
S Otálora, A Cruz-Roa, J Arevalo, M Atzori, A Madabhushi, AR Judkins, ...
Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations
N Marini, S Marchesin, S Otálora, M Wodzinski, A Caputo, ...
NPJ digital medicine 5 (1), 102, 2022
Systematic comparison of deep learning strategies for weakly supervised Gleason grading
S Otálora, M Atzori, A Khan, O Jimenez-del-Toro, V Andrearczyk, H Müller
Medical Imaging 2020: Digital Pathology 11320, 142-149, 2020
Fusing learned representations from Riesz filters and deep CNN for lung tissue classification
R Joyseeree, S Otálora, H Müller, A Depeursinge
Medical image analysis 56, 172-183, 2019
Deep multimodal case–based retrieval for large histopathology datasets
O Jimenez-del-Toro, S Otálora, M Atzori, H Müller
Patch-Based Techniques in Medical Imaging: Third International Workshop …, 2017
Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification
S Otálora, N Marini, H Müller, M Atzori
BMC Medical Imaging 21 (77), 2021
Multi-task deep learning for glaucoma detection from color fundus images
L Pascal, OJ Perdomo, X Bost, B Huet, S Otálora, MA Zuluaga
Scientific Reports 12 (1), 12361, 2022
Generalizing convolution neural networks on stain color heterogeneous data for computational pathology
A Khan, M Atzori, S Otálora, V Andrearczyk, H Müller
Medical Imaging 2020: Digital Pathology 11320, 173-186, 2020
Semi-weakly supervised learning for prostate cancer image classification with teacher-student deep convolutional networks
S Otálora, N Marini, H Müller, M Atzori
Interpretable and Annotation-Efficient Learning for Medical Image Computing …, 2020
BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources
R Ramos-Pollán, FA González, JC Caicedo, A Cruz-Roa, JE Camargo, ...
2012 IEEE 8th International Conference on E-Science, 1-8, 2012
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
N Marini, M Atzori, S Otálora, S Marchand-Maillet, H Müller
Proceedings of the IEEE/CVF International Conference on Computer Vision, 601-610, 2021
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