Machine learning in medical imaging and computer vision:
Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in he...
Gespeichert in:
| Weitere beteiligte Personen: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
2023
|
| Schriftenreihe: | Healthcare technologies series
49 |
| Links: | https://doi.org/10.1049/PBHE049E https://doi.org/10.1049/PBHE049E https://doi.org/10.1049/PBHE049E |
| Zusammenfassung: | Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. "Machine Learning in Medical Imaging and Computer Vision" provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields. |
| Umfang: | 1 Online-Ressource (xviii, 362 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781839535949 |
| DOI: | 10.1049/PBHE049E |
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| 520 | |a Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. "Machine Learning in Medical Imaging and Computer Vision" provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields. | ||
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| id | DE-604.BV049521332 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T20:14:25Z |
| institution | BVB |
| isbn | 9781839535949 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034867173 |
| oclc_num | 1422494991 |
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| owner | DE-91 DE-BY-TUM DE-706 |
| owner_facet | DE-91 DE-BY-TUM DE-706 |
| physical | 1 Online-Ressource (xviii, 362 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-100-IET ZDB-100-IET TUM_Paketkauf_2023 |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | The Institution of Engineering and Technology |
| record_format | marc |
| series2 | Healthcare technologies series |
| spellingShingle | Machine learning in medical imaging and computer vision |
| title | Machine learning in medical imaging and computer vision |
| title_auth | Machine learning in medical imaging and computer vision |
| title_exact_search | Machine learning in medical imaging and computer vision |
| title_full | Machine learning in medical imaging and computer vision edited by Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev and Farid Nait-Abdesselam |
| title_fullStr | Machine learning in medical imaging and computer vision edited by Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev and Farid Nait-Abdesselam |
| title_full_unstemmed | Machine learning in medical imaging and computer vision edited by Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev and Farid Nait-Abdesselam |
| title_short | Machine learning in medical imaging and computer vision |
| title_sort | machine learning in medical imaging and computer vision |
| url | https://doi.org/10.1049/PBHE049E |
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