Palavras-chave:
- https://publications.hevs.ch/index.php/keywords/single/197
- 3D texture
- Atlas-based segmentation
- big data
- biological organs
- biomedical MRI
- Biomedical texture analysis
- CAD
- Classification
- Clinical decision support
- computerised tomography
- data mining
- Decision-system
- Deep Learning
- detection
- Diabetes Monitoring
- digital pathology
- Distributed systems
- Epilepsy
- evaluation framework
- evaluation metrics
- Event Calculus
- feature extraction
- Image registration
- image retrieval
- image segmentation
- Intelligent Agents
- Lung graph model
- lung segmentation
- machine learning
- Medical case-based retrieval
- Medical computer vision
- medical image analysis
- Medical image analysis and retrieval
- Modular architecture
- multi-atlas based segmentation
- Natural Language Processing
- organ segmentation
- Pervasive Healthcare
- Pulmonary embolism
- Pulmonary hypertension
- Region-of-interest detection
- Riesz transform
- scalability
- segmentation
- selection
- Survival analysis
- texture analysis
- VISCERAL
- visceral-project
- whole slide imaging
- Whole-slide image classification
Publicações de Oscar Jimenez del Toro ordenado por recente
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| , , , , , , , , , , , and , Impact of CT dose on AI performance: A comparison of radiomics, deep, and foundation models in a multi-centric anthropomorphic phantom study (2026), in: Medical Physics |
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| , , , , , , , and , Influence of CT Scanners on Radiomics Features in Abdominal CT: A Multicenter Phantom Study, in: European Congress of Radiology, 2024 |
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| , , , , , , , , , and , Comparing Stability and Discriminatory Power of Handcrafted Versus Deep Radiomics: A 3D-Printed Anthropomorphic Phantom Study, in: 12th IEEE European Workshop on Visual Information Processing, páginas 62-66, 2024 |
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| , , , , , , , , and , Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses (CT-Phantom4Radiomics), [Data set], 2023 |
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| , , , , , , , , and , Breast cancer survival analysis agents for clinical decision support (2023), in: Computer Methods and Programs in Biomedicine, 231(107373) |
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| , , , and , Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors (2021), in: Journal of Medical Systems, 45:109 |
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, , , , , , , and , 3D-Printed Iodine-Ink CT Phantom for Radiomics Feature Extraction - Advantages and Challenges (2023), in: Medical Physics, 50:9(5682-5697)
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, , , , , , , , and , Assessing radiomics feature stability with simulated CT acquisitions (2022), in: Scientific Reports, 12:1(4732)
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, , , , , , , , and , The discriminative power and stability of radiomics features with CT variations: Task-based analysis in an anthropomorphic 3D-printed CT phantom (2021), in: Investigative Radiology, 56:12(820-825)
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| , , , , , , , , and , Assessment of the stability and discriminative power of radiomics features in liver lesions using an anthropomorphic 3D-printed CT phantom, in: Scientific session SGR-SSR, 2021 |
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| , , , , , , , , and , Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 |
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, , , , , and , A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT (2020), in: Computers in Biology and Medicine(103962)
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| , , , , and , A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020 |
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| , , , , and , Using Publicly Available Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction, in: Multimedia Modeling (MMM 2020), Seoul, Korea, 2020 |
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| , , , and , Exploiting the PubMed Central repository to mine out a large multimodal dataset of rare cancer studies, in: SPIE Medical Imaging, Houston, TX, USA, 2020 |
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| , , , and , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
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| , Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support, University of Geneva, 2017 |
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| , , and , A Graph Model of the Lungs with Morphology-based Structure for Tuberculosis Type Classification, in: The 26th International Conference on Information Processing in Medical Imaging (IPMI), 2019 |
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| , , , and , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2018), in: BioArXiv |
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| , , , and , From Local to Global: A Holistic Lung Graph Model, in: MICCAI 2018, Granada, Spain, 2018 |
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| , , , , , , , and , Tumor proliferation grading from whole slide images, in: SPIE Medical Imaging, Houston, Texas, USA, 2018 |
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| , , , and , Finding and Classifying Tuberculosis Types for a Targeted Treatment: MedGIFT--UPB Participation in the ImageCLEF 2017 tuberculosis Task, in: ImageCLEF working Notes 2017, Dublin, Ireland, 2017 |
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, , and , 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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| , , , , , , and , Elsevier book on Texture Analysis, capítulo Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
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| , , , , , , and , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, páginas 101400O-101400O-9, 2017 |
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, , , and , COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines (2017), in: Computers in Biology and Medicine, 81(24–31)
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, , , , , , , , , , , , , , , , , , , , , , , , , , and , Cloud–based Evaluation of Organ Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks (2016), in: IEEE Transactions on Medical Imaging
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| , and , Combining Radiology Images and Meta-data for Multimodal Medical Case-Based Retrieval, Springer, 2017 |
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| , , , and , Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, capítulo Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, Springer, 2017 |
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| , , , and , Overview of the first workshop on Multimodal Retrieval in the medical domain (MRMD 2015), Springer, 2015 |
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, , , , , , and , Creating a Large-Scale Silver Corpus from Multiple Algorithmic Segmentations, in: MICCAI medical computer vision workshop at MICCAI 2015, Munich, Germany, 2016
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, , , , , , and , Processing Diabetes Mellitus Composite Events in MAGPIE (2016), in: Journal of Medical Systems, 40:2(44)
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| , , and , Proceedings of the VISCERAL Anatomy3 Organ Segmentation Challenge, 2015 |
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| , , , , , , , , , , , , , , , , , and , Overview of the VISCERAL Challenge at ISBI 2015, in: VISCERAL Anatomy 3 proceedings, páginas 6-11, 2015 |
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, , , and , Multimodal Retrieval in the Medical Domain (MRMD), Springer, LNCS, volume 9059, 2015
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| , , , , and , Live ECG Readings Using Google Glass in Emergency Situations, in: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy, 2015 |
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| , , and , Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, páginas 22-26, CEUR-WS, 2015 |
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| , , and , RadLex Terms and Local Texture Features for Multimodal Medical Case Retrieval, in: Multimodal Retrieval in the Medical Domain (MRMD) 2015, Vienna, Austria, Springer, 2015 |
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| , , , and , Overview of the VISCERAL Retrieval Benchmark 2015, in: Multimodal Retrieval in the Medical Domain, Vienna, Austria, Springer, 2015 |
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| , , , , , , , , , , , , , and , VISCERAL-VISual Concept Extraction challenge in RAdioLogy: Organsegmentierung: Übersicht, Einblicke und erste Ergebnisse, in: Deutscher Röntgenkongress, Hamburg, Germany, 2015 |
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| , , and , Efficient and fully automatic segmentation of the lungs in CT volumes, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, New York, USA, páginas 31-35, CEUR-WS, 2015 |
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| , , , , , , , , , , , , , and , VISCERAL-VISual Concept Extraction challenge in RAdioLogy: Segmentation challenge: overview, insights and preliminary results, in: ECR, Vienna, Austria, 2015 |
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| , , , and , Workshop Multimodal Retrieval in the Medical Domain, in: ECIR, Vienna, Austria, 2015 |
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| , and , Test Data and Results of the Automatic Metric Selection Method (2014), in: tuwien.ac.at |
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| , , and , Texture classification of anatomical structures in CT using a context-free machine learning approach, in: SPIE Medical Imaging 2015, páginas 94140W-94140W-14, SPIE, 2015 |
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| and , Hierarchical Multi-atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks, in: Medical Computer Vision. Large Data in Medical Imaging, Springer, 2014 |
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| , , , , , , , , , , , , , , , and , VISCERAL - VISual Concept Extraction Challenge in RAdioLogy: ISBI 2014 Challenge Organization, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, páginas 6-15, 2014 |
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| , and , A Formal Method For Selecting Evaluation Metrics For Image Segmentation, in: IEEE International Conference on Image Processing (ICIP) 2014, Paris, France, 2014 |
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| and , Hierarchical multi-structure segmentation guided by anatomical correlations, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, páginas 32-36, 2014 |
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