Juan Sebastian Otálora Montenegro
Orcid: 0000-0003-2125-8476
According to our database1,
Juan Sebastian Otálora Montenegro
authored at least 32 papers
between 2012 and 2023.
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Bibliography
2023
Federated Learning on Heterogeneous Diffusion-Weighted Imaging Data for Acute Stroke Infarct Segmentation.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
2022
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.
npj Digit. Medicine, 2022
Weighting Schemes for Federated Learning in Heterogeneous and Imbalanced Segmentation Datasets.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022
2021
Deep Learning for Histopathology Image Analysis From Heterogeneous and Multimodal Data Sources.
PhD thesis, 2021
Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.
Medical Image Anal., 2021
Frontiers Comput. Sci., 2021
Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.
BMC Medical Imaging, 2021
H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021
Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations.
Proceedings of the MICCAI Workshop on Computational Pathology, 2021
2020
CoRR, 2020
Systematic comparison of deep learning strategies for weakly supervised Gleason grading.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
Generalizing convolution neural networks on stain color heterogeneous data for computational pathology.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks.
Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020
Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations.
Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020
Classification of Noisy Free-Text Prostate Cancer Pathology Reports Using Natural Language Processing.
Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020
2019
Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification.
Medical Image Anal., 2019
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.
Comput. Methods Programs Biomed., 2019
Proceedings of the 31st International Teletraffic Congress, 2019
Proceedings of the Computational Linguistics and Intelligent Text Processing, 2019
2018
SIGMETRICS Perform. Evaluation Rev., 2018
A Deep Learning Mechanism for Efficient Information Dissemination in Vehicular Floating Content.
CoRR, 2018
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content.
Proceedings of the Computational Pathology and Ophthalmic Medical Image Analysis, 2018
OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
2017
Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score.
Proceedings of the Medical Imaging 2017: Digital Pathology, 2017
Proceedings of the Patch-Based Techniques in Medical Imaging, 2017
Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images.
Proceedings of the Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2017
2015
Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015
2014
Proceedings of the Working Notes for CLEF 2014 Conference, 2014
2013
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2013
2012
BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources.
Proceedings of the 8th IEEE International Conference on E-Science, 2012