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.
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: Mishra Название: Explainable AI Recipes ISBN: 1484290283 ISBN-13(EAN): 9781484290286 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn * Create code snippets and explain machine learning models using Python * Leverage deep learning models using the latest code with agile implementations * Build, train, and explain neural network models designed to scale * Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI
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.
Описание: Deep Learning for EEG-based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI).
Автор: Somani Название: Interpretability in Deep Learning ISBN: 303120638X ISBN-13(EAN): 9783031206382 Издательство: Springer Рейтинг: Цена: 22359.00 р. 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.
Описание: This book constitutes the refereed joint proceedings of the 5th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2022, held in September 2022, in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022. The 10 full papers presented at iMIMIC 2022 were carefully reviewed and selected from 24 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention.
Автор: Jorge Casillas; O. Cord?n; Francisco Herrera Trigu Название: Interpretability Issues in Fuzzy Modeling ISBN: 3642057020 ISBN-13(EAN): 9783642057021 Издательство: Springer Рейтинг: Цена: 36570.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy.
Описание: The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques.
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