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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases", we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the 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.

Zustand: Gut. Zustand: Gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the 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.

Taschenbuch. Zustand: Neu. Medical Image Learning with Limited and Noisy Data | First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings | Ghada Zamzmi (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xi | Englisch | 2022 | Springer | EAN 9783031167591 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Paperback. Zustand: Brand New. 281 pages. 9.25x6.10x0.60 inches. In Stock.

Taschenbuch. Zustand: Neu. Medical Image Learning with Limited and Noisy Data | Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings | Zhiyun Xue (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xi | Englisch | 2023 | Springer | EAN 9783031471964 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 onchallenges 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.

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