Schlagworte:
- 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
- cs.CV
- data mining
- Desktop Grid
- 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
- 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
- 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
- nosocomial infection
- oncology
- organ segmentation
- Oropharynx
- radiomics
- retrieval
- Riesz
- Riesz transform
- scalability
- Security
- signal processing
- social interaction
- support vector machines
- Systematic Review
- Taverna
- technologism
- 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
Publikationen von Adrien Depeursinge sortiert nach Aktualität
, , , und , 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
|
[URL] |
, , , , , und , Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients with Surgically Treated Head and Neck Cancer: A Systematic Review (2020), in: JAMA Otolaryngology - Head and Neck Surgery, 146:5(471-479)
|
[DOI] |
| , , , , , , , und , Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 |
[URL] |
, , , und , Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)
|
[DOI] [URL] |
| , , , , , , und , An Exploration of Uncertainty Information for Segmentation Quality Assessment, in: SPIE Medical Imaging 2020: Image Processing, Houston, TX, USA, Seiten 381-390, SPIE, 2020 |
|
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
|
|
| , , , und , Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis, in: Irish Machine Vision and Image Processing Conference, Seiten 37-44, 2019 |
|
| , , , und , Integrating radiomics into holomics for personalised oncology: from algorithms to bedside (2020), in: European Radiology Experimental, 4(11) |
|
| , , , , und , Radial B-Splines for Optimal Detection in Images, in: ISBI Special Session on Spline Models in Biomedical Imaging, 2019 |
|
| , , , , , , und , How to find the best radiomics features for prediction of overall survival in SBRT for hepatocellular carcinoma?, in: European SocieTy for Radiotherapy & Oncology, 2019 |
|
, , , und , Exploring local rotation invariance in 3D CNNs with steerable filters, in: Medical Imaging with Deep Learning, Seiten 15-26, Proceedings of Machine Learning Research, 2019
|
[URL] |
| , , 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 |
|
| , , 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 |
|
| , , , , , und , Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma, in: IEEE Engineering in Medicine and Biology Conference, 2020 |
|
, , , , , , , , , , , , und , 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] |
| , , und , Fast Rotational Sparse Coding (2018)(arXiv:1806.04374) |
[URL] |
| , und , Learning Cross-Protocol Radiomics and Deep Feature Standardization from CT Images of Texture Phantoms, in: SPIE Medical Imaging 2019, International Society for Optics and Photonics, Seiten 109-116, SPIE, 2019 |
|
| und , Rotational 3D Texture Classification Using Group Equivariant CNNs (2018), in: ArXiv |
[URL] |
| , , , und , Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features, in: Machine Learning in Medical Imaging (MLMI), Seiten 107--115, Springer International Publishing, 2018 |
[URL] |
, , und , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
|
[URL] |
| , und , Neural Network Training for Cross-Protocol Radiomic Feature Standardization in Computed Tomography (2019), in: Journal of Medical Imaging, 6:3(024008) |
[URL] |
| , , , , , , , und , Holographic visualisation and interaction of fused CT, PET and MRI volumetric medical imaging data using dedicated remote GPGPU ray casting, in: Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation, Seiten 102-110, Springer International Publishing, 2018 |
|
, , , , , , , und , Signature of Survival: A 18F-FDG PET Based Whole-Liver Radiomics Analysis Predicts Survival After 90Y-TARE for Hepatocellular Carcinoma (2017), in: OncoTarget, 9:4(4549-4558)
|
|
, , , , , und , Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness (2019), in: Nature Scientific Reports, 9:1(4500)
|
|
, und , Texture-Driven Parametric Snakes for Semi-Automatic Image Segmentation (2019), in: Computer Vision and Image Understanding, 188(102793)
|
|
, , , , , , , , , 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)
|
|
| , , , , und , Comparing 18-FDG PET 3D texture attributes for the prediction of survival and recurrence in oropharyngeal cancers treated with radiotherapy, in: Workshop on the Prediction and Modeling of response to Molecular and External Beam Radiotherapies, Le Bono, France, 2017 |
|
| , und , Visual Grammar: a language modelling approach for building efficient and meaningful bags of visual words (2017), in: ArXiv(1835004) |
|
| , , , , , , 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 |
|
| , , , , , , , , und , Valeur de la TEP au 18-FDG pour prédire la récidive dans les cancers ORL non oropharyngé traités par radio-chimiothérapie, in: Société Française de radiothérapie Oncologique, 2017 |
|
| , , , , , , , , 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 |
|
, , , , und , Principled Design and Implementation of Steerable Detectors (2021), in: IEEE Transactions on Image Processing, 30(4465-4478)
|
[DOI] |
| und , Biomedical Texture Operators and Aggregation Functions: A Methodological Review and User’s Guide, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 55-94, Elsevier, 2017 |
[DOI] [URL] |
| , Multi-Scale and Multi-Directional Biomedical Texture Analysis: Finding the Needle in the Haystack, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 29-53, Elsevier, 2017 |
[DOI] [URL] |
| , und , Fundamentals of Texture Processing for Biomedical Image Analysis: A General Definition and Problem Formulation, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 1-27, Elsevier, 2017 |
[DOI] [URL] |
| , und , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, Demonstration at SPIE MI 2017, 2017 |
[URL] |
| , und , 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] |
| , , , , 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 |
[DOI] [URL] |
| , , , , , und , QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, Seiten 349-377, Elsevier, 2017 |
[URL] |
| , und , Biomedical Texture Analysis: Fundamentals, Applications and Tools, Elsevier, Elsevier-MICCAI Society Book series, 2017 |
[URL] |
| , , , , , 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) |
[DOI] [URL] |
, , , , , und , Metabolic Tumor Volume and Total Lesion Glycolysis in Oropharyngeal Cancer treated with definitive radiotherapy: Which threshold is the best predictor of local control ? (2017), in: Clinical Nuclear Medicine, 42:6(e281–e285)
|
|
, , , , , , , , , , , , und , A PET-based nomogram for oropharyngeal cancers (2017), in: European Journal of Cancer, 75(222-230)
|
|
, , , , , , , und , 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)
|
|
| , , , , und , Is tumor heterogeneity quantified by 3D texture analysis of MRI able to predict non-response to NAC in breast cancer?, in: European Society for Magnetic Resonance in Medicine and Biology, 2016 |
|
| , , , , , und , A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT images, in: MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data, Seiten 58-68, 2016 |
|
| , und , Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment, in: Medical Image Computing and Computer-Assisted Interventions (MICCAI), Seiten 619--626, Springer International Publishing, 2016 |
|
, , , , , , , und , A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT (2016), in: IEEE Transactions on Medical Imaging, 35:12(2620-2630)
|
|
| , , , , , , , , und , Locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma (2018), in: biorXiv |
[DOI] [URL] |