Schlagworte:
- adversarial learning
- Classification
- Clinical decision support
- Deep Convolutinoal Neural Network
- Deep convolutional neural network
- Deep Learning
- digital pathology
- floating content
- Histopathology
- human-machine interaction
- image retrieval
- machine learning
- multi-task learning
- Natural Language Processing
- Open access
- VANETs
- whole slide imaging
Publikationen von Sebastian Otalora sortiert nach Titel
A
| , und , A Deep Learning Mechanism for Efficient Information Dissemination in Vehicular Floating Content (2018), in: arXiv |
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| , , und , A Deep Learning Strategy for Vehicular Floating Content Management (2018), in: WAIN-PERFORMANCE |
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| , , , , , , und , A retrieval system for digital pathology for private datasets and scientific literature, in: European Congress of Digital Pathology, Helsinki, Finland, 2018 |
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| , , , , und , A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020 |
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, , , , , und , Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography (2019), in: Computer Methods and Programs in Biomedicine
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| , , und , Classification of noisy free-text prostate cancer pathology reports using natural language processing, in: Workshop AIDP at ICPR, 2021 |
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| , , , , , , , und , Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Seiten 581-588, Springer International Publishing, 2015 |
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| , , und , Combining weak and strong supervised learning improves strong supervision in Gleason pattern classification (2021), in: BMC Medical Imaging |
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| , , , , , , und , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, Seiten 101400O-101400O-9, 2017 |
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| , , , , , , , , , und , Data-driven color augmentation for H\&E stained images in computational pathology (2023), in: Journal of Pathology Informatics(100183) |
| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2018), in: BioArXiv |
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| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
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, , und , Deep Multimodal Case-Based Retrieval for Large Histopathology Datasets, in: MICCAI 2017 workshop on Patch-based image analysis, Quebec City, Canada, 2017
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| , , , , und , DeepFloat: Resource-Efficient DynamicManagement of Vehicular Floating Content (2019), in: ITC 31- Networked Systems and Services |
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| , , , , und , Determining the scale of image patches using a deep learning approach, in: IEEE International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, 2018 |
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| , , , , , , und , Elsevier book on Texture Analysis, Kapitel Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
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, , und , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
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| , , und , Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial Learning, arxiv, 2020 |
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| , , , und , H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression, in: ICCV 2021 workshop on Computational Challenges in Digital Pathology, 2021 |
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| , , , , , , und , Identification and retrieval of prostate cancer cases using a content-based search tool (2019), in: Pathology Informatics |
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| , , und , Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content, in: MICCAI 2018 - Computational Pathology Workshop (COMPAY), Granada, Spain, 2018 |
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| , , , und , Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization (2023), in: Machine Learning for Biomedical Imaging (MELBA), 2 |
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| , , , , , , , und , Multi-Scale Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations, in: COMPAY workshop at MICCAI, 2021 |
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| , , , und , Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval, in: MICCAI, Lima, Peru, 2020 |
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| , , , , , , und , Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images (2021), in: Frontiers in Computer Science |
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| , , und , OCT-NET: A convolutional network for automatic classification of normal and DME volumnes in OCT eye images, in: IEEE International Symposium on Biomedical Imaging (ISBI), Washington DC, USA, 2018 |
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| , , und , Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations, in: Workshop Artificial Intelligence for Digital Pathology, ICPR, Milano, Italy, 2021 |
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| , , und , Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on histopathology image classification (2021), in: Medical Image Analysis |
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| , , und , Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks, in: MICCAI workshop Labels, Lima, Peru, 2020 |
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, , , und , Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology (2019), in: Frontiers in Bioengineering and Biotechnology-Bioinformatics and Computational Biology
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| , , , , , , und , stainlib: a python library for augmentation and normalization of histopathology H&E images (2022), in: bioArXiv |
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| , und , Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images, in: MICCAI workshop LABELS, Springer, 2017 |
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| , , , , , , , , , , , , , , , , , , und , Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations (2022), in: Nature Partner Journal on Digital Medicine |
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