Palavras-chave:
- https://publications.hevs.ch/index.php/keywords/single/197
- 3D information retrieval
- 3D texture
- AI
- ARC
- artificial intelligence
- Atlas
- Automatic segmentation
- Benchmarking
- big data
- Biological tissue
- 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
- data mining
- Desktop Grid
- Discrete wavelet transform
- eHealth
- Epilepsy
- ethnomethodology
- evaluation
- feature extraction
- FOS: Computer and information sciences
- fracture retrieval
- Grid
- Hadoop
- head and neck cancer
- HealthGrid
- High-resolution lung CT
- Hospital
- 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
- Lesion detection
- Lesion segmentation
- lung
- Lung image
- Lung image analysis
- Lung image retrieval
- lung segmentation
- lung tissue classification
- machine learning
- Machine Learning (cs.LG)
- MapReduce
- medical image analysis
- Medical image analysis and retrieval
- medical image processing
- Medical image retrieval
- medical imaging
- Medical informatics
- Medical information retrieval
- mobile devices
- mobile information retrieval
- MRI
- multi-atlas based segmentation
- multidimensional image data analysis
- multimedia library
- Multimodal information retrieval and information fusion
- Multiple sclerosis
- nosocomial infection
- oncology
- organ segmentation
- Oropharynx
- radiomics
- retrieval
- Riesz
- Riesz transform
- scalability
- Security
- signal processing
- social interaction
- support vector machines
- Systematic Review
- Taverna
- test collection
- test collection creation including signals and images
- texture analysis
- texture classification
- user interface
- user interfaces
- User testing and task analysis
- virtualization
- visceral-project
- visual feature extraction
- visual inforamtion retrieval
- visual information retrieval
- wavelet
- wavelets
- Yearbook
Publications of Adrien Depeursinge sorted by title
N
| , , , and , Nosocomial Infection Case Reporting using Fisher's Linear Discriminant Algorithm, in: HEALTHINF 2009, páginas 317-322, 2009 |
|
| , , , , , , , , and , 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 |
|
O
, , , and , On combining visual and ontological similarities for medical image retrieval applications (2014), in: Medical Image Analysis, 18:7(1082–1100)
|
[DOI] [URL] |
| , , , and , On the Scale Invariance in State of the Art CNNs Trained on ImageNet (2021), in: Special Issue "Interpretable and Annotation-Efficient Learning for Medical Image Computing" in Machine Learning and Knowledge Extraction:3(374–391) |
|
| , and , Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop (2016), in: Journal of Imaging, 2:2(19) |
[DOI] [URL] |
| , , , , , and , Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: classification of usual interstitial pneumonia, in: IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, USA, páginas 403-406, IEEE, 2015 |
[DOI] [URL] |
| , and , Oropharynx Detection in PET-CT for Tumor Segmentation, in: Irish Machine Vision and Image Processing Conference, 2020, 2020 |
|
| , , , , , , and , Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, páginas 1-21, 2021 |
|
| , , , , , , , , , and , Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images, in: Head and Neck Tumor Segmentation and Outcome Prediction, páginas 1-37, 2022 |
[DOI] [URL] |
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT, páginas 1-30, Springer, Cham, 2023 |
[DOI] [URL] |
, , , , , , , and , Overview of the predictive value of quantitative 18 FDG PET in head and neck cancer treated with chemoradiotherapy (2016), in: Critical Reviews in Oncology/Hematology, 108(40-51)
|
|
| , , and , Overview of the Second Workshop on Medical Content--Based Retrieval for Clinical Decision Support, in: Medical Content--based Retrieval for Clinical Decision Support, Toronto, Canada, páginas 1--11, Lecture Notes in Computer Sciences (LNCS), 2012 |
[DOI] |
P
, , , , , , , , , , , , and , PET-based predictive survival model after radiotherapy for head and neck cancer (2019), in: European Journal of Nuclear Medicine and Molecular Imaging, 46:3(638-649)
|
[URL] |
| , , and , 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 |
|
| , , , , and , PET/CT Radiomics predict Pulmonary Lymphangitic Carcinomatosis (PLC) in Non-Small Cell Lung Cancer (NSCLC) (2020), in: Journal of Nuclear Medicine, 61:supplement 1(1311--1311) |
[URL] |
| , , and , Plug-in Grid: A virtualized grid Cluster, in: MICCAI workshop on HealthGrids, páginas 74--83, 2009 |
|
, , and , Predicting Adenocarcinoma Recurrence Using Computational Texture Models of Nodule Components in Lung CT (2015), in: Medical Physics, 42:4(2054-2063)
|
[DOI] [URL] |
| , , , , and , Predicting non-response to NAC in patients with breast cancer using 3D texture analysis, in: European Congress of Radiology, Vienna, Austria, 2015 |
[URL] |
| , , and , Predicting Treatment Response in Triple Negative Breast Cancer Through Quantitative Image Analysis in Perfusion MRI, in: 6th Annual Symposium of the Center for Biomedical Imaging at Stanford, Stanford, CA, USA, 2014 |
|
, , , and , Predicting Visual Semantic Descriptive Terms from Radiological Image Data: Preliminary Results with Liver Lesions in CT (2014), in: IEEE Transactions on Medical Imaging, 33:8(1-8)
|
[DOI] |
, , , , and , Principled Design and Implementation of Steerable Detectors (2021), in: IEEE Transactions on Image Processing, 30(4465-4478)
|
[DOI] |
| , , and , Prototypes for content-based image retrieval in clinical practice (2011), in: The Open Medical Informatics Journal (TOMIJ), 5(58-72) |
|
| , , , , and , Pulmonary Embolism Detection using Localized Vessel-Based Features in Dual Energy CT, in: SPIE Medical Imaging, páginas 941407-941407-10, SPIE, 2015 |
[DOI] [URL] |
| , , , and , Putting the image into perspective: The need for domain knowledge when performing image-based diagnostic aid, in: Swiss conference on medical informatics (SSIM 2006), 2006 |
|
Q
| and , Quality Assessment for Interoperable Quantitative CT imaging (QA4IQI) - Open access to standardized quantitative imaging, HES-SO Valais-Wallis, 2022 |
|
| , , , , , , , and , QuantImage v2: A Clinician-in-the-loop Cloud Platform for Radiomics Research, in: European Society of Radiology, 2022 |
|
, , , , , , , , and , QuantImage v2: A Comprehensive and Integrated Physician-Centered Cloud Platform for Radiomics and Machine Learning Research (2023), in: European Radiology Experimental, 7:16
|
|
| , , , and , QuantImage v2: an Open-Source and Web-Based Integrated Platform for Clinical Radiomics Research, in: Joint scientific session SSRMP/SGR-SSR, 2021 |
|
| , , , , , and , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, páginas 349-377, Elsevier, 2017 |
[URL] |
| , and , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, Demonstration at SPIE MI 2017, 2017 |
[URL] |
| , , , , and , 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 |
|
R
| , , , , and , Radial B-Splines for Optimal Detection in Images, in: ISBI Special Session on Spline Models in Biomedical Imaging, 2019 |
|
, , , , , and , Radiomics Analysis Using The Image Biomarker Standardization Initiative (IBSI) Benchmarks And Guidelines, in: Radiological Society of North America (RSNA) 2021 Annual Meeting, 2021
|
|
| , and , Region-based volumetric medical image retrieval, in: SPIE Medical Imaging: Advanced PACS-based Imaging Informatics and Therapeutic Applications, Orlando, FL, USA, páginas 867406-867406-10, SPIE, 2013 |
[DOI] [URL] |
| , , , , , and , Reproducibility of lung cancer radiomic features extracted from data-driven respiratory gating and free-breathing flow imaging in 18F-FDG PET/CT, in: 2022 Annual Meeting of the Society of Nuclear Medicine and Molecular Imaging (SNMMI), 2022 |
|
, , , , , and , Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT (2022), in: European Journal of Hybrid Imaging, 6:1(33)
|
|
| , , , , , , and , Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows, in: ACM CHI 2023, 2023 |
[DOI] [URL] |
, and , Retrieval of high-dimensional visual data: current state, trends and challenges ahead (2014), in: Multimedia Tools and Applications, 69:2(539-567)
|
[DOI] [URL] |
| , , , , , , , , and , Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 |
|
, , , , , and , Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness (2019), in: Nature Scientific Reports, 9:1(4500)
|
|
| , , , and , Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features, in: Machine Learning in Medical Imaging (MLMI), páginas 107--115, Springer International Publishing, 2018 |
[URL] |
| , , , , , and , Rotation-covariant texture analysis of 4D dual-energy CT as an indicator of local pulmonary perfusion, in: IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA, páginas 149-152, IEEE, 2013 |
[DOI] |
| , and , Rotation-Covariant Tissue Analysis for Interstitial Lung Diseases Using Learned Steerable Filters: Performance Evaluation and Relevance for Diagnostic Aid (2018), in: Computerized Medical Imaging and Graphics, 64(1-11) |
[DOI] |
| , , and , Rotation-covariant visual concept detection using steerable Riesz wavelets and bags of visual words, in: SPIE Wavelets and Sparsity XV, San Diego, CA, USA, páginas 885816-885816-11, SPIE, 2013 |
[DOI] [URL] |
| , and , 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 |
|
, , and , Rotation–covariant texture learning using steerable Riesz wavelets (2014), in: IEEE Transactions on Image Processing, 23:2(898-908)
|
[DOI] |
| and , Rotational 3D Texture Classification Using Group Equivariant CNNs (2018), in: ArXiv |
[URL] |
S
| , , , , and , Segmentation and Classification of Head and Neck Nodal Metastases and Primary Tumors in PET/CT, in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), páginas 4731-4735, 2022 |
|
and , Sensors, Medical Images and Signal Processing: Comprehensive Multi--modal Diagnosis Aid Frameworks (2010), in: IMIA Yearbook of Medical Informatics, 5:1(43--46)
|
|