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Author : Yuan Xue
Publisher : Springer Nature
ISBN 13 : 3031581717
Total Pages : 178 pages
Book Rating : 4.0/5 (315 download)
Download or read book Data Augmentation, Labelling, and Imperfections written by Yuan Xue and published by Springer Nature. This book was released on with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hien V. Nguyen
Publisher : Springer Nature
ISBN 13 : 303117027X
Total Pages : 134 pages
Book Rating : 4.0/5 (311 download)
Download or read book Data Augmentation, Labelling, and Imperfections written by Hien V. Nguyen and published by Springer Nature. This book was released on 2022-09-21 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.
Author : Sandy Engelhardt
Publisher : Springer Nature
ISBN 13 : 3030882101
Total Pages : 278 pages
Book Rating : 4.0/5 (38 download)
Download or read book Deep Generative Models, and Data Augmentation, Labelling, and Imperfections written by Sandy Engelhardt and published by Springer Nature. This book was released on 2021-09-29 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.
Author : Yuan Xue
Publisher : Springer
ISBN 13 : 9783031581700
Total Pages : 0 pages
Book Rating : 4.5/5 (817 download)
Download or read book Data Augmentation, Labelling, and Imperfections written by Yuan Xue and published by Springer. This book was released on 2024-06-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions. The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis.
Author : Zhiyun Xue
Publisher : Springer Nature
ISBN 13 : 3031449177
Total Pages : 274 pages
Book Rating : 4.0/5 (314 download)
Download or read book Medical Image Learning with Limited and Noisy Data written by Zhiyun Xue and published by Springer Nature. This book was released on 2023-11-08 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
Author : Hayit Greenspan
Publisher : Springer Nature
ISBN 13 : 3031439996
Total Pages : 832 pages
Book Rating : 4.0/5 (314 download)
Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 written by Hayit Greenspan and published by Springer Nature. This book was released on 2023-09-30 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Author : Ruidan Su
Publisher : Springer Nature
ISBN 13 : 9811667756
Total Pages : 567 pages
Book Rating : 4.8/5 (116 download)
Download or read book Medical Imaging and Computer-Aided Diagnosis written by Ruidan Su and published by Springer Nature. This book was released on 2024-01-20 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation.
Author : Halina Kwaśnicka
Publisher : Springer Nature
ISBN 13 : 3031373065
Total Pages : 230 pages
Book Rating : 4.0/5 (313 download)
Download or read book Advances in Smart Healthcare Paradigms and Applications written by Halina Kwaśnicka and published by Springer Nature. This book was released on 2023-08-16 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is dedicated to showcase research and innovation in smart healthcare systems and technologies led by women scientists, researchers, and practitioners. With the advent of artificial intelligence (AI) and related technologies, the healthcare sector has undergone tremendous changes in practice and management in recent years. On par to men, women have made significant contributions to tackle a variety of healthcare problems, creating smarter paradigms to provide effective and efficient solutions for patients and stakeholders. The book presents a small collection of contributions by outstanding women in STEM (Science, Technology, Engineering and Mathematics) education, focusing on the healthcare domain. The selected articles allow readers to comprehend current advances in AI and other methods for undertaking healthcare challenges. It is envisaged that the inspiring work by prominent women scientists, researchers, and practitioners reported in this book offers a beacon to propel women in pursuing STEM education and advancing the healthcare sector for the benefits of humankind.
Author : Fazlı YANIK
Publisher : Efe Akademi Yayınları
ISBN 13 : 6256504267
Total Pages : 218 pages
Book Rating : 4.2/5 (565 download)
Download or read book HEALTH& SCIENCE 2023-II written by Fazlı YANIK and published by Efe Akademi Yayınları. This book was released on 2023-06-22 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: CONTENTS CLINICAL VARIANT INTERPRETATION IN GENETICS - Mustafa Tarık ALAY INVESTIGATION OF EPIGENETICS AND GENETIC BIOMARKERS IN CANCER DIAGNOSIS: LIQUID BIOPSY - Elif ERTÜRK IMMUNOTHERAPY IN CANCER TREATMENT - Hayrani Eren BOSTANCI, Faruk Kaan ÇELİK, İnci TEMUR CHRONIC VENOUS INSUFFICIENCY AND PHYSIOTHERAPY - Sıla ÇELİK, Ömer ŞEVGİN CURRENT DIAGNOSTIC METHODS IN CORONARY ARTERY DISEASES - Ayşegül DURMAZ, Şadan YAVUZ LUMBAR TRANSFORAMINAL EPIDURAL STEROID INJECTIONS - Ahmet Tolgay AKINCI CATARACT TYPES AND CURRENT TREATMENT APPROACHES - Göksu ALAÇAMLI THE IMPORTANCE OF THE ANATOMICAL STRUCTURE OF MAXILLARY SINUS IN ORAL AND MAXILLOFACIAL SURGERY: A REVIEW OF THE LITERATURE - Raha AKBARİHAMED, Hacer EBERLİKÖSE, Arif Yiğit GÜLER CLOSED RHINOPLASTY - Ahmet KÖDER CLAVICLE FRACTURES: CURRENT CONCEPTS - Eşref SELÇUK, Cihan ÜNYILMAZ PEDIATRIC SUPRACONYLAR HUMERUS FRACTURES - Doğukan ERKAL, Murat EREM NON-PROSTHETIC TREATMENT OPTIONS FOR JOINT CARTILAGE INJURIES - Erdem ÖZDEN PERSONALIZED MEDICAL GRAFT AND GRAFT MOLDING SUPPORTED BY ARTIFİCIAL INTELLIGENCE - BAŞAK ÇAKMAK, GIRAY ÖTKEN, AHMET TOLGAY AKINCI DRUG DISCOVERY WITH COMPUTER AIDED DRUG DESIGN METHODS - Okan AYKAÇ, Burçin TÜRKMENOĞLU
Author : Honghao Gao
Publisher : Springer Nature
ISBN 13 : 3031243862
Total Pages : 544 pages
Book Rating : 4.0/5 (312 download)
Download or read book Collaborative Computing: Networking, Applications and Worksharing written by Honghao Gao and published by Springer Nature. This book was released on 2023-01-24 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNICST 460 and 461 constitutes the proceedings of the 18th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2022, held in Hangzhou, China, in October 2022. The 57 full papers presented in the proceedings were carefully reviewed and selected from 171 submissions. The papers are organized in the following topical sections: Recommendation System; Federated Learning and application; Edge Computing and Collaborative working; Blockchain applications; Security and Privacy Protection; Deep Learning and application; Collaborative working; Images processing and recognition.
Author : Sebastian Kelle
Publisher : Frontiers Media SA
ISBN 13 : 2832523706
Total Pages : 221 pages
Book Rating : 4.8/5 (325 download)
Download or read book Quantitative imaging (QI) and artificial intelligence (AI) in cardiovascular diseases written by Sebastian Kelle and published by Frontiers Media SA. This book was released on 2023-05-17 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Rikke Gade
Publisher : Springer Nature
ISBN 13 : 3031314352
Total Pages : 457 pages
Book Rating : 4.0/5 (313 download)
Download or read book Image Analysis written by Rikke Gade and published by Springer Nature. This book was released on 2023-04-26 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023. The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.
Author : Olivier Commowick
Publisher : Frontiers Media SA
ISBN 13 : 2832520375
Total Pages : 132 pages
Book Rating : 4.8/5 (325 download)
Download or read book Automatic methods for multiple sclerosis new lesions detection and segmentation written by Olivier Commowick and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Vasilis Kontonis
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (143 download)
Download or read book Learning from Imperfect Data written by Vasilis Kontonis and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The datasets used in machine learning and statistics are \emph{huge} and often \emph{imperfect},\textit{e.g.}, they contain corrupted data, examples with wrong labels, or hidden biases. Most existing approaches (i) produce unreliable results when the datasets are corrupted, (ii) are computationally inefficient, or (iii) come without any theoretical/provable performance guarantees. In this thesis, we \emph{design learning algorithms} that are \textbf{computationally efficient} and at the same time \textbf{provably reliable}, even when used on imperfect datasets. We first focus on supervised learning settings with noisy labels. We present efficient and optimal learners under the semi-random noise models of Massart and Tsybakov -- where the true label of each example is flipped with probability at most 50\% -- and an efficient approximate learner under adversarial label noise -- where a small but arbitrary fraction of labels is flipped -- under structured feature distributions. Apart from classification, we extend our results to noisy label-ranking. In truncated statistics, the learner does not observe a representative set of samples from the whole population, but only truncated samples, \textit{i.e.}, samples from a potentially small subset of the support of the population distribution. We give the first efficient algorithms for learning Gaussian distributions with unknown truncation sets and initiate the study of non-parametric truncated statistics. Closely related to truncation is \emph{data coarsening}, where instead of observing the class of an example, the learner receives a set of potential classes, one of which is guaranteed to be the correct class. We initiate the theoretical study of the problem, and present the first efficient learning algorithms for learning from coarse data.
Author : Vijaya Kumar Suda
Publisher : Packt Publishing Ltd
ISBN 13 : 1804613789
Total Pages : 398 pages
Book Rating : 4.8/5 (46 download)
Download or read book Data Labeling in Machine Learning with Python written by Vijaya Kumar Suda and published by Packt Publishing Ltd. This book was released on 2024-01-31 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.
Author : Scott Vetter
Publisher : IBM Redbooks
ISBN 13 : 0738458058
Total Pages : 64 pages
Book Rating : 4.7/5 (384 download)
Download or read book IBM Power Systems Enterprise AI Solutions written by Scott Vetter and published by IBM Redbooks. This book was released on 2019-09-25 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication helps the line of business (LOB), data science, and information technology (IT) teams develop an information architecture (IA) for their enterprise artificial intelligence (AI) environment. It describes the challenges that are faced by the three roles when creating and deploying enterprise AI solutions, and how they can collaborate for best results. This publication also highlights the capabilities of the IBM Cognitive Systems and AI solutions: IBM Watson® Machine Learning Community Edition IBM Watson Machine Learning Accelerator (WMLA) IBM PowerAI Vision IBM Watson Machine Learning IBM Watson Studio Local IBM Video Analytics H2O Driverless AI IBM Spectrum® Scale IBM Spectrum Discover This publication examines the challenges through five different use case examples: Artificial vision Natural language processing (NLP) Planning for the future Machine learning (ML) AI teaming and collaboration This publication targets readers from LOBs, data science teams, and IT departments, and anyone that is interested in understanding how to build an IA to support enterprise AI development and deployment.
Author : Duc Haba
Publisher : Packt Publishing Ltd
ISBN 13 : 1803235918
Total Pages : 394 pages
Book Rating : 4.8/5 (32 download)
Download or read book Data Augmentation with Python written by Duc Haba and published by Packt Publishing Ltd. This book was released on 2023-04-28 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore beautiful, customized charts and infographics in full color Work with fully functional OO code using open source libraries in the Python Notebook for each chapter Unleash the potential of real-world datasets with practical data augmentation techniques Book Description Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You'll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you'll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques. What you will learn Write OOP Python code for image, text, audio, and tabular data Access over 150,000 real-world datasets from the Kaggle website Analyze biases and safe parameters for each augmentation method Visualize data using standard and exotic plots in color Discover 32 advanced open source augmentation libraries Explore machine learning models, such as BERT and Transformer Meet Pluto, an imaginary digital coding companion Extend your learning with fun facts and fun challenges Who this book is for This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book.
