Schlagworte:
- 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
Publikationen von Oscar Jimenez del Toro sortiert nach Titel
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3
, , , , , , , und , 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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[DOI] |
A
| , und , A Formal Method For Selecting Evaluation Metrics For Image Segmentation, in: IEEE International Conference on Image Processing (ICIP) 2014, Paris, France, 2014 |
[DOI] [URL] |
| , , und , 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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, , , , , und , 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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| , , , , 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 , Assessing radiomics feature stability with simulated CT acquisitions (2022), in: Scientific Reports, 12:1(4732)
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| , , , , , , , , und , 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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B
| , , , , und , Benefits of texture analysis of dual energy CT for computer-aided pulmonary embolism detection, in: The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka, Japan, Seiten 3973-3976, 2013 |
[DOI] |
| , , , , , , , , und , Breast cancer survival analysis agents for clinical decision support (2023), in: Computer Methods and Programs in Biomedicine, 231(107373) |
[DOI] [URL] |
C
, , , , , , , , , , , , , , , , , , , , , , , , , , und , Cloud–based Evaluation of Organ Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks (2016), in: IEEE Transactions on Medical Imaging
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[DOI] [URL] |
| , , , und , Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors (2021), in: Journal of Medical Systems, 45:109 |
[DOI] |
| , und , Combining Radiology Images and Meta-data for Multimodal Medical Case-Based Retrieval, Springer, 2017 |
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| , , , , , , , , , und , 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, Seiten 62-66, 2024 |
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, , , und , 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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[DOI] [URL] |
| , , , , , , 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 |
[DOI] [URL] |
, , , , , , und , 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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D
| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
[DOI] |
| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2018), in: BioArXiv |
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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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E
| , , und , 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, Seiten 31-35, CEUR-WS, 2015 |
[URL] |
| , , , , , , und , Elsevier book on Texture Analysis, Kapitel Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
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| , , , und , Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Seiten 353-360, Springer Berlin Heidelberg, 2013 |
[DOI] [URL] |
| , , , und , 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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F
| , , , und , 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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| , , , und , From Local to Global: A Holistic Lung Graph Model, in: MICCAI 2018, Granada, Spain, 2018 |
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H
| , , und , 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, Seiten 22-26, CEUR-WS, 2015 |
[URL] |
| und , 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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| und , Hierarchical multi-structure segmentation guided by anatomical correlations, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, Seiten 32-36, 2014 |
[URL] |
I
| , , , , , , , , , , , und , 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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| , , , , , , , und , Influence of CT Scanners on Radiomics Features in Abdominal CT: A Multicenter Phantom Study, in: European Congress of Radiology, 2024 |
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L
| , , , , und , 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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M
| und , Multi atlas–based segmentation with data driven refinement, in: Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, Valencia, Spain, Seiten 605-608, IEEE, 2014 |
[DOI] |
und , Multi-Structure Atlas-Based Segmentation Using Anatomical Regions Of Interest, in: Medical Computer Vision. Large Data in Medical Imaging, Nagoya, Japan, Seiten 217-221, Springer International Publishing, 2014
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[DOI] [URL] |
, , , und , Multimodal Retrieval in the Medical Domain (MRMD), Springer, LNCS, Band 9059, 2015
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[DOI] |
O
, , , , , und , Overview of the 2013 workshop on Medical Computer Vision (MCV 2013), in: Medical Computer Vision. Large Data in Medical Imaging, Seiten 3-10, Springer International Publishing, 2014
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[DOI] [URL] |
| , , , und , Overview of the first workshop on Multimodal Retrieval in the medical domain (MRMD 2015), Springer, 2015 |
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| , , , , , , , , , , , , , , , , , und , Overview of the VISCERAL Challenge at ISBI 2015, in: VISCERAL Anatomy 3 proceedings, Seiten 6-11, 2015 |
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| , , , und , Overview of the VISCERAL Retrieval Benchmark 2015, in: Multimodal Retrieval in the Medical Domain, Vienna, Austria, Springer, 2015 |
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P
| , , und , Proceedings of the VISCERAL Anatomy3 Organ Segmentation Challenge, 2015 |
[URL] |
, , , , , , und , Processing Diabetes Mellitus Composite Events in MAGPIE (2016), in: Journal of Medical Systems, 40:2(44)
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[DOI] [URL] |
Q
| , Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support, University of Geneva, 2017 |
[URL] |
R
| , , und , 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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| , , , und , Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, Kapitel Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, Springer, 2017 |
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| , , , , , , , , und , 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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T
| , , , , , , , , und , Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses (CT-Phantom4Radiomics), [Data set], 2023 |
[DOI] |
| , und , Test Data and Results of the Automatic Metric Selection Method (2014), in: tuwien.ac.at |
[URL] |
| , , und , Texture classification of anatomical structures in CT using a context-free machine learning approach, in: SPIE Medical Imaging 2015, Seiten 94140W-94140W-14, SPIE, 2015 |
[DOI] [URL] |
, , , , , , , , und , 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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| , , , , , , , und , Tumor proliferation grading from whole slide images, in: SPIE Medical Imaging, Houston, Texas, USA, 2018 |
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| 1-50 | 51-58 |