Résultats de la recherche - "generative adversarial networks"

Advanced Machine Learning Applications in Big Data Analytics

Sujets: “…generative adversarial network…”
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On the acceptance of "fake" histopathology: A study on frozen sections optimized with deep learning par Mario Siller, Lea Maria Stangassinger, Christina Kreutzer, Peter Boor, Roman D. Bulow, Theo J.F. Kraus, Saskia von Stillfried, Soraya Wolfl, Sebastien Couillard-Despres, Gertie Janneke Oostingh, Anton Hittmair, Michael Gadermayr

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On the acceptance of "fake" histopathology: A study on frozen sections optimized with deep learning par Mario Siller, Lea Maria Stangassinger, Christina Kreutzer, Peter Boor, Roman D Bulow, Theo J F Kraus, Saskia von Stillfried, Soraya Wolfl, Sebastien Couillard-Despres, Gertie Janneke Oostingh, Anton Hittmair, Michael Gadermayr

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Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking par Xin Liu, Bin Zheng, Xiaoqin Duan, Wenjing He, Yuandong Li, Jinyu Zhao, Chen Zhao, Lin Wang

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Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review par Umer Saeed, Syed Yaseen Shah, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah

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Generative artificial intelligence: synthetic datasets in dentistry par Fahad Umer, Niha Adnan

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Multimedia Forensics

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Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges par Yan Chen, Pouyan Esmaeilzadeh

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Predicting antibody binders and generating synthetic antibodies using deep learning par Yoong Wearn Lim, Adam S. Adler, David S. Johnson

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A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment par Issam Boukhennoufa, Delaram Jarchi, Xiaojun Zhai, Victor Utti, Saeid Sanei, Tracey K. M. Lee, Jo Jackson, Klaus D. McDonald-Maier

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Multimedia Forensics

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Computational Methods for Medical and Cyber Security

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Medical image analysis using deep learning algorithms par Mengfang Li, Yuanyuan Jiang, Yanzhou Zhang, Haisheng Zhu

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Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data par S. M. Vidanagamachchi, K. M. G. T. R. Waidyarathna

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Tackling stain variability using CycleGAN-based stain augmentation par Nassim Bouteldja, David L. Hölscher, Roman D. Bülow, Ian S.D. Roberts, Rosanna Coppo, Peter Boor

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Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution par Cyrus Manuel, Philip Zehnder, Sertan Kaya, Ruth Sullivan, Fangyao Hu

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Machine Learning for Brain Disorders

Table des matières: “…A Non-Technical Introduction to Machine Learning -- Classic Machine Learning Methods -- Deep Learning: Basics and Convolutional Neural Networks (CNN) -- Recurrent Neural Networks (RNN) - Architectures, Training Tricks, and Introduction to Influential Research -- Generative Adversarial Networks and Other Generative Models -- Transformers and Visual Transformers -- Clinical Assessment of Brain Disorders -- Neuroimaging in Machine Learning for Brain Disorders -- Electroencephalography and Magnetoencephalography -- Working with Omics Data, An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science -- Electronic Health Records as Source of Research Data -- Mobile Devices, Connected Objects, and Sensors -- Medical Image Segmentation using Deep Learning -- Image Registration: Fundamentals and Recent Advances Based on Deep Learning -- Computer-Aided Diagnosis and Prediction in Brain Disorders -- Subtyping Brain Diseases from Imaging Data -- Data-Driven Disease Progression Modelling -- Computational Pathology for Brain Disorders -- Integration of Multimodal Data -- Evaluating Machine Learning Models and their Diagnostic Value -- Reproducibility in Machine Learning for Medical Imaging -- Interpretability of Machine Learning Methods Applied to Neuroimaging -- A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging -- Main Existing Datasets for Open Brain Research on Humans -- Machine Learning for Alzheimer's Disease and Related Dementias -- Machine Learning for Parkinson's Disease and Related Disorders -- Machine Learning in Neuroimaging of Epilepsy -- Machine Learning in Multiple Sclerosis -- Machine Learning for Cerebrovascular Disorders -- The Role of Artificial Intelligence in Neuro-Oncology Imaging -- Machine Learning for Neurodevelopmental Disorders -- Machine Learning and BrainImaging for Psychiatric Disorders: New Perspectives.…”
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Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review par Phawinpon Chotwanvirat, Aree Prachansuwan, Pimnapanut Sridonpai, Wantanee Kriengsinyos

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xxAI - Beyond Explainable AI International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers /

Table des matières: “…Editorial -- xxAI - Beyond explainable Artificial Intelligence -- Current Methods and Challenges -- Explainable AI Methods - A Brief Overview -- Challenges in Deploying Explainable Machine Learning -- Methods for Machine Learning Models -- CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations -- New Developments in Explainable AI -- A Rate-Distortion Framework for Explaining Black-box Model Decisions -- Explaining the Predictions of Unsupervised Learning Models -- Towards Causal Algorithmic Recourse -- Interpreting Generative Adversarial Networks for Interactive Image Generation -- XAI and Strategy Extraction via Reward Redistribution -- Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis -- Interpreting and improving deep-learning models with reality checks -- Beyond the Visual Analysis of Deep Model Saliency -- ECQ^2: Quantization for Low-Bit and Sparse DNNs -- A whale's tail - Finding the right whale in an uncertain world -- Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science -- An Interdisciplinary Approach to Explainable AI.…”
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