| 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 | 100 | 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 | 97 | 2018 |
| 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 | 95 | 2019 |
| 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 | 94 | 2017 |
| 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 7, 198, 2019 | 76 | 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 | 71 | 2016 |
| 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 | 59 | 2021 |
| 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 | 50 | 2017 |
| 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 | 47 | 2022 |
| 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 | 46 | 2019 |
| 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 | 35 | 2015 |
| 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 | 34 | 2022 |
| Data-driven color augmentation for H&E stained images in computational pathology N Marini, S Otalora, M Wodzinski, S Tomassini, AF Dragoni, ... Journal of Pathology Informatics 14, 100183, 2023 | 30 | 2023 |
| 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 | 27 | 2021 |
| Multi-scale task multiple instance learning for the classification of digital pathology images with global annotations N Marini, S Otálora, F Ciompi, G Silvello, S Marchesin, S Vatrano, ... MICCAI Workshop on Computational Pathology, 170-181, 2021 | 26 | 2021 |
| 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 | 26 | 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 | 24 | 2017 |
| 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 | 23 | 2020 |
| 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 | 22 | 2021 |
| Multi_scale_tools: a python library to exploit multi-scale whole slide images N Marini, S Otálora, D Podareanu, M van Rijthoven, J van der Laak, ... Frontiers in Computer Science 3, 684521, 2021 | 21 | 2021 |
Henning MüllerProfessor in computer science at HES-SO & in radiology at the University of Genevahevs.ch의 이메일 확인됨