All publications sorted by recency
| 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 |
| , , , , , , , , , , and , Apparent motion perception in upper limb amputees with phantom sensations: obstacle shunning and obstacle tolerance, in: Hand, Brain and Technology: The Somatosensory System, Monte Verità, Switzerland, 2018 |
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| , , , , , , and , Fusion of Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema (DME) Detection on Optical Coherence Tomography (OCT) Images, in: EMBC 2018, Honolulu, HI, USA, 2018 |
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| , , and , Deep Features and Data Reduction for Classification of SD-OCT Images: Application to Diabetic Macular Edema, in: ICIAS 2018, Kuala Lumpur, Malaysia, 2018 |
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| , , , and , Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data, in: Workshop Learning Applications for Intelligent Autonomous Robots, 15th International Conference on Intelligent Autonomous Systems, Baden-Baden, Germany, 2018 |
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| , , , , , , , , , , , , and , Myo-electricity and gaze tracking data to improve hand prosthetics and neuro-cognitive examination, in: FESSH 2018, Copenhagen, Denmark, 2018 |
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| , , , , , , , , , , , , , , , , , , and , Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation, in: CLEF conference proceeding, Avignon, France, Springer, 2018 |
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| , , , , , , 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 |
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| , , and , Overview of the ImageCLEF 2018 caption prediction tasks, in: CLEF working notes, CEUR, 2018 |
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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 , Automated diabetic macular edema (DME) analysis using fine tuning with Inception-Resnet-v2 on OCT images, in: IECBES, Malaysia, 2018 |
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| , , , , , , , , , , , , , , , , , , , , and , How to exploit weaknesses in biomedical challenge design and organization, in: MICCAI 2018, Granada, Spain, Springer, 2018 |
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| , , , , and , Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task, in: CLEF 2018 working notes, Avignon, France, CEUR, 2018 |
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| and , Deep Multimodal Classification of Image Types in Biomedical Journal Figures, in: CLEF 2018, Avignon, France, Springer, 2018 |
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| and , Texture-based Graph Model of the Lungs for Drug Resistance Detection, Tuberculosis Type Classification, and Severity Scoring: Participation in ImageCLEF 2018 Tuberculosis Task, in: CLEF working notes, Avignon, France, CEUR, 2018 |
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| , and , Textured Graph--based Model of the Lungs: Application on Tuberculosis Type Classification and Multi--Drug Resistance Prediction, in: CLEF 2018, Avignon, France, 2018 |
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| , , , and , From Local to Global: A Holistic Lung Graph Model, in: MICCAI 2018, Granada, Spain, 2018 |
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| , , and , Overview of ImageCLEFtuberculosis 2018 - Detecting multi-drug resistance, classifying tuberculosis type, and assessing severity score, in: CLEF working notes, CEUR, 2018 |
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| , , and , A multi -criteria decision making approach for ranking wheelchair accessible tourism, in: Digital, Innovation, Entrepreneurship & Financing 2018 conference, Lyon, France, 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, pages 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), pages 107--115, Springer International Publishing, 2018 |
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| , , , , and , Sensory dominance and multisensory integration as screening tools in aging (2018), in: Scientific Reports, 8(1-11) |
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| , and , The Control of Selective Attention and Emerging Mathematical Cognition: Beyond Unidirectional Influences, in: Heterogeneity of Function in Numerical Cognition, pages 111--126, Elsevier, 2018 |
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| , , and , Are we ready for real-world neuroscience? (2018), in: Journal of Cognitive Neuroscience |
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| , Specyfika przetwarzania wielozmysłowego, pages 97-105, 2009 |
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| , , , , and , What makes medical students better listeners? (2016), in: Current Biology, 26:13(R519--R520) |
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| , , and , What's what in auditory cortices? (2018), in: NeuroImage, 176(29--40) |
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| , , , , and , Cue-dependent circuits for illusory contours in humans (2016), in: NeuroImage, 129(335--344) |
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| , , , , , and , The dual nature of early-life experience on somatosensory processing in the human infant brain (2017), in: Current Biology, 27:7(1048--1054) |
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| , and , A multisensory perspective on object memory (2017), in: Neuropsychologia, 105(243--252) |
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| , and , Neuroplasticity: Unexpected consequences of early blindness (2015), in: Current Biology, 25:20(R998--R1001) |
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| , , , , and , The COGs (context, object, and goals) in multisensory processing (2016), in: Experimental brain research, 234:5(1307--1323) |
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| , , and , Top-down control and early multisensory processes: chicken vs. egg (2015), in: Frontiers in integrative neuroscience, 9(17) |
| , , , , and , The multisensory function of the human primary visual cortex (2016), in: Neuropsychologia, 83(161--169) |
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| , and , The context-contingent nature of cross-modal activations of the visual cortex (2016), in: Neuroimage, 125(996--1004) |
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| , , and , Contextual factors multiplex to control multisensory processes (2016), in: Human brain mapping, 37:1(273--288) |
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| , , , , and , The role of auditory cortices in the retrieval of single-trial auditory--visual object memories (2015), in: European Journal of Neuroscience, 41:5(699--708) |
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| , and , Multisensory context portends object memory (2014), in: Current Biology, 24:16(R734--R735) |
| , , , , and , Multi-modal distraction: Insights from children’s limited attention (2015), in: Cognition, 136(156--165) |
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| and , Top-down control of audiovisual search by bimodal search templates (2013), in: Psychophysiology, 50:10(996--1009) |
| and , Multisensory enhancement of attentional capture in visual search (2011), in: Psychonomic bulletin \& review, 18:5(904) |
| , , and , Individual differences in emotional reactivity moderate the strength of the relationship between attentional and implicit-memory biases towards threat-related stimuli (2015), in: Journal of Cognitive Psychology, 27:6(715--724) |
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| , and , Attention-driven bias for threat-related stimuli in implicit memory. Preliminary results from the Posner cueing paradigm (2010), in: Polish Psychological Bulletin, 41:4(163--171) |
| , and , Kwestionariusz Potrzeby Poznania--konstrukcja i weryfikacja empiryczna narzedzia mierzacego motywacje poznawcz{\k{a}} (2011), in: Psychologia Społeczna, 62:17(113--128) |
| , , , , , and , Timing Reliability for Local Schedulers in Multi-Agent Systems (2018), in: In Proceedings of first International Workshop on Real-Time compliant Multi-Agent Systems |
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| , , , and , A Task-sets Generator for Supporting the Analysis of Multi-Agent Systems under General Purpose and Real-Time Conditions, in: In Proceedings of first International Workshop on Real-Time compliant Multi-Agent Systems, 2018 |
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| , and , Social Network Chatbots for Smoking Cessation:Towards a Multi-Agent Framework, in: In proceedings of International Conference on Web and Social Media 2018, 2018 |
| , and , Decision Tree Ensemble vs. N.N. Deep Learning: Efficiency Comparison for a Small Image Dataset, Jakarta, Indonesia, Inproceedings of the 2018 International Workshop on Big Data and Information Security, 2018 |
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| 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 |