Schlagworte:
- https://publications.hevs.ch/index.php/keywords/single/197
- 3D imaging
- 3D information retrieval
- 3D texture
- 82Rb PET/CT
- AI
- ARC
- artificial intelligence
- Atlas
- Automatic segmentation
- Benchmarking
- big data
- Biological tissue
- Brain
- CAD
- case-based retrieval
- Challenge
- Classification
- clinical data
- clinical data analysis
- clinical workflows
- Computer Vision and Pattern Recognition (cs.CV)
- computer-aided diagnosis
- computerised tomography
- computing infrastructures
- Content-based image retrieval
- conversation analysis
- Cortical lesions
- Cox Model
- cs.CV
- data mining
- Deep convolutional neural network
- Deep Learning
- Desktop Grid
- detection
- Discrete wavelet transform
- eHealth
- Epilepsy
- ethnomethodology
- evaluation
- exoticism
- feature extraction
- FOS: Computer and information sciences
- fracture retrieval
- Grid
- Hadoop
- head and neck cancer
- Healthcare
- HealthGrid
- High-resolution lung CT
- Hospital
- Human-Centered Computing
- image acquisition
- image analysis
- image classif
- image classification
- Image databases
- image processing
- image retrieval
- image storage
- ImageCLEF
- information fusion
- information retrieval
- information retrieval evaluation
- information retrieval literature
- Information Systems
- Infrastructures for computation
- interstitial lung diseases
- Left Ventricle Segmentation
- Lesion detection
- Lesion segmentation
- lung
- Lung image
- Lung image analysis
- Lung image retrieval
- lung segmentation
- lung tissue classification
- machine learning
- Machine Learning (cs.LG)
- Magnetic resonance imaging
- MapReduce
- medical image analysis
- Medical image analysis and retrieval
- medical image processing
- Medical image retrieval
- medical imaging
- Medical informatics
- Medical information retrieval
- mentalism
- mobile devices
- mobile information retrieval
- MRI
- multi-atlas based segmentation
- multidimensional image data analysis
- multimedia library
- Multimodal information retrieval and information fusion
- Multiple sclerosis
- Myocardial Blood Flow
- nosocomial infection
- oncology
- organ segmentation
- Oropharynx
- radiomics
- retrieval
- Riesz
- Riesz transform
- Rubidium PET
- scalability
- Security
- segmentation
- signal processing
- social interaction
- support vector machines
- Systematic Review
- Taverna
- technologism
- test collection
- test collection creation including signals and images
- texture analysis
- texture classification
- Transmural Perfusion
- Trustworthy AI
- user interface
- user interfaces
- User testing and task analysis
- virtualization
- visceral-project
- visual feature extraction
- visual inforamtion retrieval
- visual information retrieval
- wavelet
- wavelets
- Yearbook
Publikationen von Adrien Depeursinge sortiert nach erstem Autor
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| , , , , , , , , , , , , , , , , , , und , A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis (2026), in: NeuroImage: Clinical, 50(104007) |
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| , , , , , , , , , , , , , , , und , Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors, 2025 |
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| , , , , , , , , und , Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation (2025), in: Computers in Biology and Medicine |
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| und , La recherche d’images en plusieurs dimensions (2011), in: Market/IBCom |
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| , und , Faciliter l'utilisation des grilles de calcul dans le domaine biomédical: Le projet KnowARC, in: Journées francophones d'informatique médicale, 2007 |
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| , , , und , Medical visual information retrieval: from techniques to applications and evaluation, in: The 2nd International Conference on Advanced Information and Telemedicine Technologies for Health (AITTH 2008), Seiten 77-81, 2008 |
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| , , , , , und , Content-based image retrieval from a database of fracture images, in: Medical Imaging 2007: PACS and Imaging Informatics, Seiten 65160H, 2007 |
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, und , Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring (2012), in: IMIA Yearbook of Medical Informatics, 7:1(100-103)
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, , und , Automated Object Extraction for Medical Image Retrieval Using the Insight Toolkit (ITK)., in: AIRS, Seiten 476-488, 2006
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| , , , , und , KnowARC: Enabling Grid Networks for the Biomedical Research Community, in: Healthgrid 2007, Seiten 261-268, 2007 |
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| , , , , und , Medical Visual Information Retrieval: State of the Art and Challenges Ahead, in: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Seiten 683-686, 2007 |
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| , , , und , Building a Community Grid for Medical Image Analysis inside a Hospital, a Case Study, in: Medical imaging on grids: achievements and perspectives (Grid Workshop at MICCAI 2008), Seiten 3-12, 2008 |
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| , , und , Plug-in Grid: A virtualized grid Cluster, in: MICCAI workshop on HealthGrids, Seiten 74--83, 2009 |
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| , , , , , , , und , Integrating MRI and PET/CT Radiomics for Enhanced Survival Prediction in Esophageal Cancer, in: European Congress of Radiology, 2025 |
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| , , , und , Guide for radiologists and nuclear medicine physicians for a standardized radiomics analysis, in: Swiss Congress of Radiology (SCR) 2023, 2023 |
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| , und , Mobile image upload for radiology, in: Studies in Health Technology and Informatics, Copenhagen, Denmark, Seiten 1003, 2013 |
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, , , und , 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
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, , , , , , , , , , , , , , , , , , , , , , , , , und , Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge (2022), in: Medical Image Analysis, 77(102336)
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| , , , und , Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net, in: Medical Imaging with Deep Learning, 2022 |
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| , , und , PET/CT Radiomics Analysis Contributes to Detection of Pulmonary Lymphangitic Carcinomatosis (PLC) in Non-Small Cell Lung Cancer (NSCLC), in: Swiss Congress of Radiology, 2019 |
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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 , A radiomics-based analysis of functional dopaminergic scintigraphic imaging for the diagnosis of dementia with Lewy bodies (2025), in: Neurodegenerative Diseases |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , A Multimodal and Multi-centric Head and Neck Cancer Dataset for Tumor Segmentation and Outcome Prediction (2025) |
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| , , , , , , , , und , Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses (CT-Phantom4Radiomics), [Data set], 2023 |
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| , , , , und , Web-Based Tools for Exploring the Potential of Quantitative Imaging Biomarkers in Radiology: Intensity and Texture Analysis on the ePAD Platform, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 379-410, Elsevier, 2017 |
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| , und , Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop (2016), in: Journal of Imaging, 2:2(19) |
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| , , , , , , , und , QuantImage v2: A Clinician-in-the-loop Cloud Platform for Radiomics Research, in: European Society of Radiology, 2022 |
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| , , , und , QuantImage v2: an Open-Source and Web-Based Integrated Platform for Clinical Radiomics Research, in: Joint scientific session SSRMP/SGR-SSR, 2021 |
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| , , , , , und , (18F)-FDG PET/CT parameters to predict survival and recurrence in patients with locally advanced cervical cancer treated with chemoradiotherapy (2018), in: Cancer / Radiothérapie, 22:3(229-235) |
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| , , , , , , und , 18-FDG PET-CT parameters to predict survival and recurrence in cervical cancer patients treated with chemo-radiotherapy, in: European Society for Radiotherapy and Oncology, Vienna, 2017 |
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| , , , , , , , , und , Nouveaux paramètres métaboliques du FDG-PET/TDM pour prédire la récurrence et la survie des cancers du col utérin traité par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
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| , , , , und , Quantitative Image Texture Analysis Predicts Malignancy on Multiparametric Prostate MRI, in: 91st Annual Meeting of the Western Section of American Urological Association, Indian Wells, CA, USA, 2015 |
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| , , , , , , , , , , und , Deep-PRL: a deep learning network for the identification of paramagnetic rim lesions in multiple sclerosis, in: ISMRM 2025, 2025 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Identification of paramagnetic rim lesions using conventional MRI - a deep learning approach, in: 39th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2023 |
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, , , , , , und , How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review (2023), in: NeuroImage: Clinical, 39(103491)
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| , , , , , , , , , , , , und , A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis (2026), in: Multiple Sclerosis Journal, 32:6(13524585261421491) |
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| , , , , , , , , , , , und , Foundation Model–Enhanced Classification of Paramagnetic Rim Lesions in Multiple Sclerosis, MSToronto2026 – 10th Joint ACTRIMS-ECTRIMS Meeting, 2026 |
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| , , , , , , , , , , , und , Explainability in automatic Paramagnetic Rim Lesion classification, in: 40th Congress Of The European Committee For Treatment And Research In Multiple Sclerosis (ECTRIMS), 2024 |
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| , , , , , , und , Exploiting XAI maps to improve MS lesion segmentation and detection in MRI, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI, 2025 |
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| , , , , , , , und , Instance-level quantitative saliency in multiple sclerosis lesion segmentation (2026), in: Nature Scientific Reports, 16(6917) |
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| , , , , , , , und , Instance-level explanations in multiple sclerosis lesion segmentation: a novel localized saliency map, in: ISMRM 2024, 2024 |
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| , , , , , , , und , FLAIR vs MPRAGE contribution to white matter lesion automatic segmentation in MS using localized saliency maps, in: Bern Interpretable AI Symposium (BIAS), 2023 |
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, , , , , , , , , und , The Use of Texture Based Radiomics CT Analysis to Predict Outcomes in Early-Stage Non-Small Cell Lung Cancer Treated with Stereotactic Ablative Radiotherapy (2018), in: The British Journal of Radiology, 92:1094(20180228)
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| , , , , , , , , , und , RDTA-08 MULTI-SEQUENTIAL STEREOTACTIC RADIOSURGERY (SRS) FOR BRAIN METASTASES: 10-YEAR EXPERIENCE FROM THE CHUV (LAUSANNE, SWITZERLAND) BRAIN METASTASIS CLINIC (2025), in: Neuro-Oncology Advances, 7:Supplement_2(ii26-ii26) |
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| , , , , , , und , MRI and CT radiomics for the diagnosis of acute pancreatitis (2025), in: European Journal of Radiology Open, 14 |
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, , , , und , Steer'n'Detect: Fast 2D Template Detection with Accurate Orientation Estimation (2022), in: Bioinformatics, 38:11(3146–3148)
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, , , , , und , Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI) Benchmarks And Guidelines, in: Radiological Society of North America (RSNA) 2021 Annual Meeting, 2021
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| , und , Rotation-invariant non-local means based on Riesz pyramid features and SURE parameter selection, in: 84th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2013), Novi Sad, Serbia, 2013 |
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| , , und , A lung graph model for the classification of interstitial lung disease on CT images, in: SPIE Medical Imaging 2019: Computer-Aided Diagnosis, International Society for Optics and Photonics, Seiten 869-876, SPIE, 2019 |
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