Palavras-chave:
- 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
Publicações de Adrien Depeursinge ordenado por recente
, , , , , , , , 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 , 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) |
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| , , , and , 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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| , , , , , , , , 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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| Head and Neck Tumor Segmentation, Springer International Publishing, 2021 |
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| , , , , , , and , Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT, páginas 1-21, 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 , The Importance of Feature Aggregation in Radiomics: A Head and Neck Cancer Study (2020), in: Nature Scientific Reports, 10:19679
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| , , , and , Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging, in: Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020, 2020 |
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| , and , Oropharynx Detection in PET-CT for Tumor Segmentation, in: Irish Machine Vision and Image Processing Conference, 2020, 2020 |
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| , , and , Consistency of Scale Covariance in Internal Representations of CNNs, in: Irish Machine Vision and Image Processing Conference, 2020 |
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| , , , , , , and , Impact of a Gaussian filter applied to post-reconstruction PET on radiomic features in assessing tumor heterogeneity in breast cancer. (2020), in: Journal of Nuclear Medicine, 61:supplement 1(612--612) |
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| , , , , 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) |
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| , , , , , , and , Impact of a Gaussian filter applied to post-reconstruction PET images on radiomic features to predict complete pathological response in breast cancer (2020), in: Journal of Nuclear Medicine, 61:supplement 1(606--606) |
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| , , , , , , and , Standardised convolutional filtering for radiomics, 2020 |
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, , , and , 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis, 2020
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, , , , , and , 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)
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| , , , , , , , and , Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans, in: Medical Imaging with Deep Learning, Montréal, Canada, 2020 |
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, , , and , Local Rotation Invariance in 3D CNNs (2020), in: Medical Image Analysis, 65(101756)
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| , , , , , , and , An Exploration of Uncertainty Information for Segmentation Quality Assessment, in: SPIE Medical Imaging 2020: Image Processing, Houston, TX, USA, páginas 381-390, SPIE, 2020 |
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, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
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| , , , and , Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis, in: Irish Machine Vision and Image Processing Conference, páginas 37-44, 2019 |
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| , , , and , Integrating radiomics into holomics for personalised oncology: from algorithms to bedside (2020), in: European Radiology Experimental, 4(11) |
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| , , , , and , Radial B-Splines for Optimal Detection in Images, in: ISBI Special Session on Spline Models in Biomedical Imaging, 2019 |
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| , , , , , , and , How to find the best radiomics features for prediction of overall survival in SBRT for hepatocellular carcinoma?, in: European SocieTy for Radiotherapy & Oncology, 2019 |
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, , , and , Exploring local rotation invariance in 3D CNNs with steerable filters, in: Medical Imaging with Deep Learning, páginas 15-26, Proceedings of Machine Learning Research, 2019
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| , , and , 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, páginas 869-876, SPIE, 2019 |
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| , , 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 |
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| , , , , , and , 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 |
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, , , , , , , , , , , , 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)
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| , , and , Fast Rotational Sparse Coding (2018)(arXiv:1806.04374) |
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| , and , 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, páginas 109-116, SPIE, 2019 |
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| and , Rotational 3D Texture Classification Using Group Equivariant CNNs (2018), in: ArXiv |
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| , , , 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 |
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, , and , Fusing Learned Representations from Riesz and Deep CNNs for Lung Tissue Classification (2019), in: Medical Image Analysis, 56(172-183)
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| , and , Neural Network Training for Cross-Protocol Radiomic Feature Standardization in Computed Tomography (2019), in: Journal of Medical Imaging, 6:3(024008) |
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| , , , , , , , and , 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, páginas 102-110, Springer International Publishing, 2018 |
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, , , , , , , and , 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)
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, , , , , and , Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness (2019), in: Nature Scientific Reports, 9:1(4500)
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, and , Texture-Driven Parametric Snakes for Semi-Automatic Image Segmentation (2019), in: Computer Vision and Image Understanding, 188(102793)
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, , , , , , , , , and , 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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| , , , , and , 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 |
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| , and , Visual Grammar: a language modelling approach for building efficient and meaningful bags of visual words (2017), in: ArXiv(1835004) |
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| , , , , , , and , 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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| , , , , , , , , and , 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 |
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| , , , , , , , , 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 |
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, , , , and , Principled Design and Implementation of Steerable Detectors (2021), in: IEEE Transactions on Image Processing, 30(4465-4478)
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| and , Biomedical Texture Operators and Aggregation Functions: A Methodological Review and User’s Guide, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, páginas 55-94, Elsevier, 2017 |
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| , Multi-Scale and Multi-Directional Biomedical Texture Analysis: Finding the Needle in the Haystack, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, páginas 29-53, Elsevier, 2017 |
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| , and , Fundamentals of Texture Processing for Biomedical Image Analysis: A General Definition and Problem Formulation, in: Biomedical Texture Analysis: Fundamentals, Applications and Tools, páginas 1-27, Elsevier, 2017 |
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