All publications sorted by author
| 1-50 | 51-100 | 101-150 | 151-200 | 201-250 | 251-300 | 301-350 | 351-400 | 401-450 | 451-500 | 501-550 | 551-600 | 601-650 | 651-700 | 701-750 | 751-800 | 801-850 | 851-900 | 901-950 | 951-1000 | 1001-1050 | 1051-1100 | 1101-1150 | 1151-1200 | 1201-1250 | 1251-1300 | 1301-1350 | 1351-1400 | 1401-1450 | 1451-1500 | 1501-1550 | 1551-1600 | 1601-1650 | 1651-1700 | 1701-1750 | 1751-1800 | 1801-1850 | 1851-1900 | 1901-1950 | 1951-2000 | 2001-2050 | 2051-2100 | 2101-2150 | 2151-2200 | 2201-2250 | 2251-2300 | 2301-2350 | 2351-2400 | 2401-2450 | 2451-2500 | 2501-2550 | 2551-2600 | 2601-2650 | 2651-2700 | 2701-2750 | 2751-2800 | 2801-2850 | 2851-2900 | 2901-2950 | 2951-3000 | 3001-3015 |
J
| , , and , Electronic Government einführen und entwickeln: Von der Idee zur Praxis, "", 2003 |
| , and , Die Koordinationsplattform in der lernenden Region – Sensibilisierung, Koordination und Qualifizierung, in: "", 2006 |
| , , , , , , , , and , Revealing most suitable CT radiomics features for retrospective studies with heterogeneous datasets, in: European Congress of Radiology (ECR) 2021, ONLINE edition, 2021 |
|
, , , , , , , , 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)
|
|
| , , , , , , , , , and , Comparing Stability and Discriminatory Power of Handcrafted Versus Deep Radiomics: A 3D-Printed Anthropomorphic Phantom Study, in: 12th IEEE European Workshop on Visual Information Processing, pages 62-66, 2024 |
|
| , , , , , , and , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, pages 101400O-101400O-9, 2017 |
[DOI] [URL] |
| , , , , , , , , 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 |
|
| , , and , RadLex Terms and Local Texture Features for Multimodal Medical Case Retrieval, in: Multimodal Retrieval in the Medical Domain (MRMD) 2015, Vienna, Austria, Springer, 2015 |
|
| , and , Combining Radiology Images and Meta-data for Multimodal Medical Case-Based Retrieval, Springer, 2017 |
|
| , , and , Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3, in: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI, pages 22-26, CEUR-WS, 2015 |
[URL] |
, , , , , and , A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT (2020), in: Computers in Biology and Medicine(103962)
|
[DOI] [URL] |
| , , and , Texture classification of anatomical structures in CT using a context-free machine learning approach, in: SPIE Medical Imaging 2015, pages 94140W-94140W-14, SPIE, 2015 |
[DOI] [URL] |
| , , , and , Overview of the VISCERAL Retrieval Benchmark 2015, in: Multimodal Retrieval in the Medical Domain, Vienna, Austria, Springer, 2015 |
|
| , , , and , Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, pages 353-360, Springer Berlin Heidelberg, 2013 |
[DOI] [URL] |
| , , , , , , , , , , , , , , , and , VISCERAL - VISual Concept Extraction Challenge in RAdioLogy: ISBI 2014 Challenge Organization, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, pages 6-15, 2014 |
[URL] |
| and , Hierarchical Multi-atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks, in: Medical Computer Vision. Large Data in Medical Imaging, Springer, 2014 |
|
| and , Multi atlas–based segmentation with data driven refinement, in: Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, Valencia, Spain, pages 605-608, IEEE, 2014 |
[DOI] |
| and , Hierarchical multi-structure segmentation guided by anatomical correlations, in: Proceedings of the VISCERAL Challenge at ISBI, Beijing, China, pages 32-36, 2014 |
[URL] |
| , , , and , Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, chapter Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark, Springer, 2017 |
|
and , Multi-Structure Atlas-Based Segmentation Using Anatomical Regions Of Interest, in: Medical Computer Vision. Large Data in Medical Imaging, Nagoya, Japan, pages 217-221, Springer International Publishing, 2014
|
[DOI] [URL] |
| , , , , , , and , Elsevier book on Texture Analysis, chapter Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
|
, , and , Deep Multimodal Case-Based Retrieval for Large Histopathology Datasets, in: MICCAI 2017 workshop on Patch-based image analysis, Quebec City, Canada, 2017
|
|
, , , , , , , , , , , , , , , , , , , , , , , , , , and , Cloud–based Evaluation of Organ Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks (2016), in: IEEE Transactions on Medical Imaging
|
[DOI] [URL] |
| , Quantitative analysis of medical images: finding relevant regions-of-interest for medical decision support, University of Geneva, 2017 |
[URL] |
| , , , and , Responsible Research and Innovation in the Digital Age (2017), in: Communications of the ACM |
[URL] |
| , "Booster" L'innovation énergétique (2015), in: Valais Valeur ajoutée |
|
| , L'Energy Living Lab, un écosystème d'innovation pour la transition énergétique, Journée internationale de la sociologie de l'énergie, Tours, 2015 |
|
| , L'Energy Living Lab pour booster l'innovation énergétique (2015), in: UVAM Tribune(p.47) |
|
| , L'usager au centre de la réflexion (2015), in: Bulletin electrosuisse/VSE/AES |
|
| , BusiNETvs: Développement durable, Banque Cantonale du Valais, HES-SO Valais, 2011 |
|
| , , , , and , Energy management in a public building: A case study co-designing the building energy management system, 2017 |
[URL] |
| , and , L’Energy Living Lab : un écosystème d’innovation pour favoriser la co-création de services énergétiques, Association française du Marketing, Marrakesh, 2015 |
| , and , How to keep a living lab alive (2015), in: Journal INFO |
[URL] |
| , and , How to keep a living lab alive, European Network of Living Labs, Amsterdam, 2014 |
[URL] |
| , and , Investigating transnational philanthropy in hospitality industry, 5th International tourism congress, The image and sustainability of tourist destination, 2011 |
| , and , Investigating the added value of a guest donation program (hotels that help) in hospitality enterprises, Advances in hospitality and tourism marketing and management, conference proceedings, 2011 |
| , , , , , , , , , , , , , , , , , , , , and , Overview of LifeCLEF 2023: evaluation of AI models for the identification and prediction of birds, plants, snakes and fungi, in: CLEF 2023 proceedings, Thessaloniki, Greece, 2023 |
| , , , , , , , and , Biodiversity information retrieval through content-based identification: a long-term evaluation, Springer, 2019 |
|
| , , , , , , , and , Are species identification tools biodiversity-friendly, in: ACM Multimedia Workshop on Multimedia Analysis of Ecological Data, Barcelona, Spain, 2014 |
|
| , , , , , , , and , Are multimedia identification tools biodiversity-friendly, in: ACM Multimedia Workshop on Multimedia Analysis of Ecological Data, Barcelona, Spain, 2013 |
|
| , , , , , , and , Overview of LifeCLEF 2018: a large-scale evaluation of species identification and recommendation algorithms in the era of AI, in: CLEF conference proceeding, Avignon, France, Springer, 2018 |
|
| , , , , , , , and , LifeCLEF 2019: Biodiversity Identification and Prediction Challenges, in: ECIR 2019, Cologne, Germany, 2019 |
|
| , , , , , , , , , and , Overview of LifeCLEF 2019: a new snapshot of the performance of species identification and prediction algorithms, in: CLEF conference Proceedings, Lugano, Switzerland, Springer LNCS, 2019 |
|
| , , , , and , Implicit biodiversity monitoring from mobile search logs (2016), in: ACM Multimedia |
|
| , , , , , , , , and , LifeCLEF 2016: Multimedia Life Species Identication Challenges, in: CLEF Proceedings, Springer, 2016 |
|
, , , , , , , , and , LifeCLEF 2017 Lab Overview: multimedia species identification challenges, in: CLEF 2017 Proceedings, 2017
|
|
| , , , , , , , , , , , , , , and , LifeCLEF 2020 Teaser: Biodiversity Identification and Prediction Challenges, in: ECIR 2020, Lisbon, Portugal, Springer LNCS, 2020 |
|
| , , , , , , , , , , , , , , , , and , Overview of LifeCLEF 2020: a System-oriented Evaluation of Automated Species Identification and Species Distribution Prediction, Thessaloniki, Greece, Springer LNCS, 2020 |
|
| , , , , , , , and , LifeCLEF 2022 Teaser: an evaluation ofMachine-Learning based Species Identification and Species Distribution Prediction, in: ECIR 2022, Stavanger, Norway, 2022 |
|
| , , , , , and , LifeCLEF 2023 teaser: Species Identification and Prediction Challenges, in: ECIR 2023, Dublin, Ireland, 2023 |
| 1-50 | 51-100 | 101-150 | 151-200 | 201-250 | 251-300 | 301-350 | 351-400 | 401-450 | 451-500 | 501-550 | 551-600 | 601-650 | 651-700 | 701-750 | 751-800 | 801-850 | 851-900 | 901-950 | 951-1000 | 1001-1050 | 1051-1100 | 1101-1150 | 1151-1200 | 1201-1250 | 1251-1300 | 1301-1350 | 1351-1400 | 1401-1450 | 1451-1500 | 1501-1550 | 1551-1600 | 1601-1650 | 1651-1700 | 1701-1750 | 1751-1800 | 1801-1850 | 1851-1900 | 1901-1950 | 1951-2000 | 2001-2050 | 2051-2100 | 2101-2150 | 2151-2200 | 2201-2250 | 2251-2300 | 2301-2350 | 2351-2400 | 2401-2450 | 2451-2500 | 2501-2550 | 2551-2600 | 2601-2650 | 2651-2700 | 2701-2750 | 2751-2800 | 2801-2850 | 2851-2900 | 2901-2950 | 2951-3000 | 3001-3015 |