國立虎尾科技大學 |

Artificial Intelligence in Medical Imaging = Opportunities, Applications and Risks /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Intelligence in Medical Imaging/ edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra.
其他題名:	Opportunities, Applications and Risks /
其他作者:	Ranschaert, Erik R.
面頁冊數:	XV, 373 p. 104 illus., 81 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Radiology. -
電子資源:	https://doi.org/10.1007/978-3-319-94878-2
ISBN:	9783319948782

Artificial Intelligence in Medical Imaging = Opportunities, Applications and Risks /
Artificial Intelligence in Medical ImagingOpportunities, Applications and Risks /[electronic resource] :edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra. - 1st ed. 2019. - XV, 373 p. 104 illus., 81 illus. in color.online resource.

PART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

ISBN: 9783319948782

Standard No.: 10.1007/978-3-319-94878-2doiSubjects--Topical Terms:

673943
Radiology.

LC Class. No.: R895-920

Dewey Class. No.: 616.0757

Artificial Intelligence in Medical Imaging = Opportunities, Applications and Risks /
LDR:03717nam a22003975i 4500 001 1007154
003 DE-He213
005 20200702154652.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783319948782 $9 978-3-319-94878-2
024 7 $a 10.1007/978-3-319-94878-2 $2 doi
035 $a 978-3-319-94878-2
050 4 $a R895-920
072 7 $a MMPH $2 bicssc
072 7 $a MED008000 $2 bisacsh
072 7 $a MKSH $2 thema
072 7 $a MKS $2 thema
082 0 4 $a 616.0757 $2 23
245 1 0 $a Artificial Intelligence in Medical Imaging $h [electronic resource] : $b Opportunities, Applications and Risks / $c edited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XV, 373 p. 104 illus., 81 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a PART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.
520 $a This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
650 0 $a Radiology. $3 673943
650 0 $a Computers. $3 565115
650 0 $a Health informatics. $3 1064466
650 1 4 $a Imaging / Radiology. $3 669876
650 2 4 $a Information Systems and Communication Service. $3 669203
650 2 4 $a Health Informatics. $3 593963
700 1 $a Ranschaert, Erik R. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300872
700 1 $a Morozov, Sergey. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300873
700 1 $a Algra, Paul R. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300874
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319948775
776 0 8 $i Printed edition: $z 9783319948799
856 4 0 $u https://doi.org/10.1007/978-3-319-94878-2
912 $a ZDB-2-SME
912 $a ZDB-2-SXM
950 $a Medicine (SpringerNature-11650)
950 $a Medicine (R0) (SpringerNature-43714)