Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019).- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification.- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis.- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection.- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules.- Deep neural network or dermatologist?.- Towards Interpretability of Segmentation Networks by analyzing DeepDreams.- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019).- Towards Automatic Diagnosis from Multi-modal Medical Data.- Deep Learning based Multi-Modal Registration for Retinal Imaging.- Automated Enriched Medical Concept Generation for Chest X-ray Images.
Автор: Reyes Mauricio, Henriques Abreu Pedro, Cardoso Jaime Название: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th Internat ISBN: 3030874435 ISBN-13(EAN): 9783030874438 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: iMIMIC 2021 Workshop.- Interpretable Deep Learning for Surgical Tool Management.- Soft Attention Improves Skin Cancer Classification Performance.- Deep Gradient based on Collective Arti cial Intelligence for AD Diagnosis and Prognosis.- This explains That: Congruent Image-Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks.- Visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions.- The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data.- Voxel-level Importance Maps for Interpretable Brain Age Estimation.- TDA4MedicalData Workshop.- Lattice Paths for Persistent Diagrams.- Neighborhood complex based machine learning (NCML) models for drug design.- Predictive modelling of highly multiplexed tumour tissue images by graph neural networks.- Statistical modeling of pulmonary vasculatures with topological priors in CT volumes.- Topological Detection of Alzheimer's Disease using Betti Curves.
Автор: Nandi Anirban, Pal Aditya Kumar Название: Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods ISBN: 1484278011 ISBN-13(EAN): 9781484278017 Издательство: Неизвестно Рейтинг: Цена: 55170.00 T Наличие на складе: Невозможна поставка. Описание: Chapter 1: Introduction to Machine Learning DomainChapter Goal: The book's opening chapter will talk about the journey of machine learning models and why model interpretability became so important in the recent times. This chapter will also cover some of the basic black box modelling algorithms in brief Sub-Topics: - Journey of machine learning domain- Journey of machine learning algorithms - Why only reporting accuracy is not enough for models Chapter 2: Introduction to Model InterpretabilityChapter Goal: This chapter will talk about the importance and need of interpretability and how businesses employ model interpretability for their decisionsSub-Topics: - Why is interpretability needed for machine learning models- Motivation behind using model interpretability- Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency- Get a definition of interpretability and learn about the groups leading interpretability research Chapter 3: Machine Learning Interpretability TaxonomyChapter Goal: A machine learning taxonomy is presented in this section. This will be used to characterize the interpretability of various popular machine learning techniques.Sub topics: - Understanding and trust- A scale for interpretability- Global and local interpretability- Model-agnostic and model-specific interpretability Chapter 4: Common Properties of Explanations Generated by Interpretability MethodsChapter goal: The purpose of this chapter to explain readers about evaluation metrics for various interpretability methods. This will help readers understand which methods to choose for specific use cases Sub topics: - Degree of importance - Stability- Consistency - Certainty- Novelty Chapter 5: Timeline of Model interpretability Methods DiscoveryChapter goal: This chapter will talk about the timeline and will give details about when most common methods of interpretability were discovered Chapter 6: Unified Framework for Model ExplanationsChapter goal: Each method is determined by three choices: how it handles features, what model behavior it analyzes, and how it summarizes feature influence. The chapter will focus in detail about each step and will try to map different methods to each step by giving detailed examplesSub topics1: - Removal based explanations- Summarization based explanations Chapter 7: Different Types of Removal Based ExplanationsChapter goal: This chapter will talk about the different types of removal based methods and how to implement them along with details of examples and Python packages, real life use cases etc.Sub topics: - IME(2009)- IME(2010)- QII- SHAP- KernelSHAP- TreeSHAP- LossSHAP- SAGE- Shapley- Shapley- Permutation- Conditional- Feature- Univariate- L2X- INVASE- LIME- LIME- PredDiff- Occlusion- CXPlain- RISE- MM- MIR- MP- EP- FIDO-CA Chapter 8: Different Types of Summarization Based ExplanationsChapter goal: This chapter will talk about the different types of summarization based methods and how to implement them along with details of examples and python p
Автор: Lepore Название: Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches ISBN: 3031124014 ISBN-13(EAN): 9783031124013 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
Автор: Somani Название: Interpretability in Deep Learning ISBN: 303120638X ISBN-13(EAN): 9783031206382 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.
Автор: Meyes Название: Transparency and Interpretability for Learned Representations of Artificial Neural Networks ISBN: 365840003X ISBN-13(EAN): 9783658400033 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI’s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed light on how to adopt an empirical neuroscience inspired approach to investigate a neural network’s learned representation in the same spirit as neuroscientific studies of the brain.
Название: Artificial intelligence and machine learning in 2d/3d medical image processing ISBN: 0367374358 ISBN-13(EAN): 9780367374358 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Medical image fusion is a process which merges information from multiple images of the same scene. The fused image provides appended information that can be utilized for more precise localization of abnormalities. The use of medical image processing databases will help to create and develop more accurate and diagnostic tools.
Автор: Rajshree Srivastava, Pradeep Kumar Mallick, Siddha Название: Computational Intelligence for Machine Learning and Healthcare Informatics ISBN: 3110647826 ISBN-13(EAN): 9783110647822 Издательство: Walter de Gruyter Цена: 136310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: Ramgopal Kashyap, A.V. Senthil Kumar Название: Challenges and Applications for Implementing Machine Learning in Computer Vision ISBN: 1799801837 ISBN-13(EAN): 9781799801832 Издательство: Mare Nostrum (Eurospan) Цена: 173190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 46470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
Автор: Andreas Miroslaus Wichert, Luis Sa-couto Название: Machine Learning - A Journey To Deep Learning: With Exercises And Answers ISBN: 9811234051 ISBN-13(EAN): 9789811234057 Издательство: World Scientific Publishing Рейтинг: Цена: 158400.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.Related Link(s)
Автор: Julio C. Rodriguez-Quinonez, Oleg Sergiyenko, Wendy Flores-Fuentes Название: Examining Optoelectronics in Machine Vision and Applications in Industry 4.0 ISBN: 1799865231 ISBN-13(EAN): 9781799865230 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 175560.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Focuses on the examination of emerging technologies for the design, fabrication, and implementation of optoelectronic sensors, devices, and systems in a machine vision approach to support industrial, commercial, and scientific applications.
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