Institute of Informatics (II)
Tópico: MedGIFT
Publications for topic "MedGIFT" ordenado por recente
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, , , , , and , 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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| , , and , Consistency of Scale Covariance in Internal Representations of CNNs, in: Irish Machine Vision and Image Processing Conference, 2020 |
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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 , 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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, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and , Standardized quantitative radiomics for high-throughput image-based phenotyping (2020), in: Radiology, 295:2(328-338)
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| , , , and , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
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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 , 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 , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2018), in: BioArXiv |
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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 , Distributed container-based evaluation platform for private/large datasets, in: ISPDC 2018, Geneva, Switzerland, 2018 |
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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 , 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 , 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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| , 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) |
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| , , , , , 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 |
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| , and , Biomedical Texture Analysis: Fundamentals, Applications and Tools, Elsevier, Elsevier-MICCAI Society Book series, 2017 |
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| , , , , , , and , Elsevier book on Texture Analysis, capítulo Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
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| , , , , , , and , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, páginas 101400O-101400O-9, 2017 |
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, , , , , and , 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)
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| , , , , and , Bodily obstacles in mental imagery. Specific perspective-dependent effects of the apparent motion perception of an actors’ body parts (2016), in: 12th Symposium of the Zurich Center for Integrative Human Physiology (ZIHP) |
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| , , , , , , , , , , and , ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview (2016), in: ICPR 2016 |
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| and , VISCERAL: Evaluation-as-a-Service for Medical Imaging, VISCERAL book, Springer, 2017 |
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| , , and , Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation, in: MICCAI workshop Labels, Springer, 2016 |
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| , , , and , Reports on CBMI 16 and ICME 16 (2016), in: IEEE Multimedia |
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| , and , Using Smart Glasses in Medical Emergency Situations, a Qualitative Pilot Study, in: WirelessHealth 2016, Bethesda, páginas 1-5, 2016 |
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| , and , Plateforme d'Aide aux Ambulanciers, in: 28eme Conférence Francophone sur l'Interaction Homme-Machine, Fribourg, Switzerland, páginas 2, 2016 |
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, , , , , , , , , , , , and , A PET-based nomogram for oropharyngeal cancers (2017), in: European Journal of Cancer, 75(222-230)
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| , , and , Electromyography for Hand Prosthetics Demo, in: XXVIII Conférence francophone sur l'Interaction Homme-Machine, Fribourg, Suisse, 2016 |
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, and , Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography (2016), in: Frontiers in Neurorobotics, 10(9)
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| , , , , and , Implicit biodiversity monitoring from mobile search logs (2016), in: ACM Multimedia |
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| , , , , , , , , , , , , , , , and , General Overview of ImageCLEF at the CLEF 2016 Labs, in: CLEF Proceedings, Springer, 2016 |
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