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Full list in Google scholar.
I've reviewed manuscripts for several journals and conferences in the area of biomedical image analysis, including for the MICCAI conference (2019,2020,2021) , and journals such as PLOS One, Medical Image Analysis and the IEEE Transactions on Medical Imaging, among others.
2021
Otálora, S., Marini, N., Müller, H., & Atzori, M. (2021). Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification. BMC Medical Imaging, 21(1), 1-14.
Marini, N., Atzori, M., Otálora, S., Marchand-Maillet, S., & Muller, H. (2021). H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 601-610).
Niccolò Marini*, Sebastian Otálora*, Henning Müller, Manfredo Atzori. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification. *Equally contributed. Medical Image Analysis. Vol 73. 2021. Open access. (This paper gathers what we learn from the previously published workshops at ICPR and MICCAI).
Niccolò Marini, Sebastian Otálora, Henning Muller, and Manfredo Atzori. Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations. International Workshop on AI for Digital Pathology. AIDP 2021. Draft.
Anjani Dhrangadhariya, Sebastian Otálora, Manfredo Atzori, and Henning Muller. Classification of noisy free-text prostate cancer pathology reports using natural language processing. International Workshop on AI for Digital Pathology. AIDP 2021. Draft.
2020
Sebastian Otálora, Niccolò Marini, Henning Müller and Manfredo Atzori. Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks. The 5th MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis LABELS 2020. Draft. People Choice Award!.
Juan S. Lara, Victor H. Contreras O., Sebastián Otálora, Henning Müller, Fabio A. González . Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval. MICCAI. 2020. Draft.
Sebastian Otálora, Manfredo Atzori, Amjad Khan, Oscar Jimenez-del-Toro, Vincent Andrearczyk and Henning Müller. A systematic comparison of deep learning strategies for weakly supervised Gleason grading. SPIE Medical Imaging 2020: Digital Pathology 2020. Draft.
Amjad Khan, Manfredo Atzori, Sebastian Otálora, Vincent Andrearczyk and Henning Müller. Generalizing convolution neural networks on stain color heterogeneous data for computational pathology. SPIE Medical Imaging 2020: Digital Pathology 2020. Online.
2019
Sebastian Otálora, Manfredo Atzori, Vincent Andrearczyk, Amjad Khan and Henning Müller. Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology. Front. Bioeng. Biotechnol., 23 August 2019. Open.
Roger Schaer, Sebastian Otalora, Oscar Jimenez-del-Toro, Manfredo Atzori and Henning Müller. Deep learning based retrieval system for gigapixel histopathology cases and open access literature. Journal of Pathology Informatics (In press). 2019. Demo.
Ranveer Joyseeree, Sebastian Otálora, Henning Müller and Adrien Depeursinge. Fusing Learned Representations from Riesz Filters and Deep CNNs for Lung Tissue Classification. Medical Image Analysis. Volume 56, August 2019, Pages 172-183. Draft.
Oscar Perdomo, Hernán Rios, Francisco Rodríguez, Sebastian Otálora, Fabrice Meriaudeau, Henning Muller, Fabio Gonzalez. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. (In press). Computer methods and programs in Biomedicine. 2019. Draft.
2018
Sebastian Otálora, Vincent Andrearczyk, Manfredo Atzori and Henning Müller. BiWGAN: Learning stable adversarial representations for prostate histopathology images. Medical Imaging meets Deep Learning MISS 2018 Summer School. 29 July - 4 Aug 2018 Favignana, Italy.[Poster].
Sebastian Otálora, Roger Schaer, Oscar Jimenez-del-Toro, Manfredo Atzori, Henning Müller. Deep learning based retrieval system for gigapixel histopathology cases and open access literature. [bioaRxiv preprint].
Sebastian Otálora, Manfredo Atzori, Vincent Andrearczyk, and Henning Müller. Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content. Proceedings of MICCAI Workshop on Computational pathology (COMPAY), Granada, Spain. September 2018. [Draft].
Sebastian Otálora, et. al. Determining the scale of image patches using a Deep Learning Approach. ISBI 2018. Washington D.C., USA, 2018 [Draft].
Oscar Perdomo, Sebastian Otálora, OCT-NET: A convolutional network for automatic classification of normal and diabetic macular edema using SD-OCT volumes. ISBI 2018. Washington D.C., USA, 2018 [Draft].
2017
Sebastian Otálora, et. al. Training Convolutional Neural Networks with Active Learning for exudate classification in eye fundus images. Proceedings of MICCAI 2017 Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis LABELS17, Quebec City, Canada, 2017 [PDF].
Oscar Jimenez–del–Toro, Manfredo Atzori, Sebastian Otálora, Mats Andersson et al. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. SPIE Medical Imaging V10140. [PDF].
Oscar Jimenez Del Toro, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Mikael Rousson, Henning Müller and Manfredo Atzori. Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning. Elsevier book on Texture Analysis, chapter Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 [Draft] .
Oscar Jimenez del Toro, Sebastian Otálora, Manfredo Atzori and Henning Müller, Deep Multimodal Case-Based Retrieval for Large Histopathology Datasets, in: MICCAI 2017 workshop on Patch-based image analysis (PatchMI), Quebec City, Canada, 2017.
2016
Oscar Perdomo, Sebastian Otálora, Fabio A.González. A Novel Machine Learning Model Based On Exudate Localization To Detect Diabetic Macular Edema. 3rd MICCAI Workshop on Ophthalmic Image Analysis - 2016, the 19th International Conference on Medical Image Computing and Computer Assisted Intervention. Athens, Greece. October 17:21 - 2016, in proceedings. [PDF]
2015
Sebastian Otálora, Angel Cruz-Roa, John Arevalo, Manfredo Atzori, Anant Madabhushi, Alexander Judkins, Fabio A.González, Henning Müller and Adrien Depeursinge. Anaplastic Medulloblastoma tumor differentiation by combining Unsupervised Feature Learning and Riesz wavelets for histopathology image representation. MICCAI 2015, the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. Munich, Germany. October 5th to 9th, in proceedings. [PDF]
2014
John Arevalo, Sebastian Otálora, Julien Wist and Fabio A. González. Automatic Infrared spectroscopy signal analysis with unsupervised feature learning and neural networks. 9th Colombian Computing Congress. Pereira, Colombia, September 3-5, 2014 9ccc proceedings. [PDF]
Eliana Sanandres, Sebastian Otálora, Carlo Tognato and Fabio A. González. El caso de chevron-texaco en ecuador: una aplicación de topic modeling para el estudio del trauma. I Congreso Internacional de Movimientos Sociales, Universidad del Norte. Barranquilla, Colombia [DRAFT][SLIDES]
Jorge A. Vanegas, John Arevalo, Sebastian Otálora, Fabián Páez, Santiago A. Pérez-Rubiano, and Fabio A. González. MindLab at ImageCLEF 2014: Scalable Concept Image Annotation. CLEF (Working Notes) 2014: 404-410 [PDF]
2013
Sebastian Otálora, Santiago A. Pérez-Rubiano and Fabio A. González. Online Matrix Factorization for Space Embedding Multilabel Annotation. 18th Iberoamerican Congress on Pattern Recognition. Havana, Cuba, November 20-23, 2013 CIARP 2013 proceedings. [PDF]
2012
Raúl Ramos-Pollán, Fabio A. González, Juan C. Caicedo, Ángel Cruz-Roa, Jorge Camargo, Jorge Vanegas, Santiago A. Pérez, David Bermeo, Sebastián Otálora, John Arévalo, Paola Katherine Rozo Bernal. BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources.8th IEEE International Conference on eScience. 8-12 October 2012. Chicago (Estados Unidos).[PDF]
2011
Santiago Pérez, Sebastián Otálora, Jorge Camargo and Fabio González. Explorando grandes colecciones de imágenes de histología a través de factores latentes.7th International Seminar on Medical Information Processing and Analysis -SIPAIM 2011. 5-7 December 2011. Universidad Industrial de Santander. Bucaramanga (Colombia). [PDF][VIDEO]