Sebastian Otálora

发表

Staining Invariant Features for Improving Generalization of Deep Convolutional Neural Networks in Computational Pathology

Henning Müller, Manfredo Atzori, Vincent Andrearczyk, 2019, Front. Bioeng. Biotechnol..

A Novel Machine Learning Model Based on Exudate Localization to Detect Diabetic Macular Edema

Oscar J. Perdomo, F. González, Sebastian Otálora, 2016 .

Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

Manfredo Atzori, Henning Müller, Roger Schaer, 2018, bioRxiv.

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images

Manfredo Atzori, Jeroen van der Laak, Damian Podareanu, 2021, Frontiers in Computer Science.

Learning Interpretable Diagnostic Features of Tumor by Multi-task Adversarial Training of Convolutional Networks: Improved Generalization

H. Müller, S. Marchand-Maillet, V. Andrearczyk, 2022 .

Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning

H. Müller, V. Andrearczyk, Sebastian Otálora, 2020, ArXiv.

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Damian Podareanu, J. A. van der Laak, M. Atzori, 2022, npj Digital Medicine.

Multi-task deep learning for glaucoma detection from color fundus images

Maria A. Zuluaga, Oscar J. Perdomo, Sebastian Otálora, 2022, Scientific Reports.

stainlib: a python library for augmentation and normalization of histopathology H&E images

Damian Podareanu, J. A. van der Laak, D. Tellez, 2022, bioRxiv.

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization

S. Marchand-Maillet, V. Andrearczyk, Sebastian Otálora, 2022 .

Data-driven color augmentation for H&E stained images in computational pathology

S. Marchand-Maillet, M. Atzori, Henning Müller, 2023, Journal of pathology informatics.

H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression

Stéphane Marchand-Maillet, Manfredo Atzori, Sebastian Otálora, 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification

Manfredo Atzori, Sebastian Otálora, Niccolò Marini, 2021, Medical Image Anal..

Generalizing convolution neural networks on stain color heterogeneous data for computational pathology

Henning Müller, Manfredo Atzori, Vincent Andrearczyk, 2020, Medical Imaging: Digital Pathology.

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

Manfredo Atzori, Sebastian Otálora, Niccolò Marini, 2021, BMC Medical Imaging.

Deep Multimodal Case-Based Retrieval for Large Histopathology Datasets

Manfredo Atzori, Henning Müller, Oscar Alfonso Jiménez del Toro, 2017, Patch-MI@MICCAI.

Analysis of Histopathology Images

Henning Müller, Manfredo Atzori, Oscar Jimenez-del-Toro, 2017 .

Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations

H. Müller, M. Atzori, Sebastian Otálora, 2020, ICPR Workshops.

Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval

Fabio A. González, Henning Müller, Sebastian Otálora, 2020, MICCAI.

Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks

Manfredo Atzori, Henning Müller, Sebastian Otálora, 2020, iMIMIC/MIL3iD/LABELS@MICCAI.

OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes

Fabio A. González, Fabrice Mériaudeau, Henning Müller, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations

H. Müller, M. Atzori, F. Ciompi, 2021, COMPAY@MICCAI.

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

Henning Müller, Ranveer Joyseeree, Adrien Depeursinge, 2019, Medical Image Anal..

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

Mats Andersson, Manfredo Atzori, Henning Müller, 2017, Medical Imaging.

Chapter 10 – Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning

Manfredo Atzori, Henning Müller, Oscar Jimenez-del-Toro, 2017 .

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization

S. Marchand-Maillet, V. Andrearczyk, Sebastian Otálora, 2020, Machine Learning for Biomedical Imaging.

Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature

Henning Müller, Manfredo Atzori, Roger Schaer, 2019, Journal of pathology informatics.

DeepFloat: Resource-Efficient Dynamic Management of Vehicular Floating Content

Marco Ajmone Marsan, Torsten Braun, Gianluca Rizzo, 2019, 2019 31st International Teletraffic Congress (ITC 31).