000 01203nam a22002897a 4500
005 20251023142717.0
008 251023b |||||||| |||| 00| 0 eng d
020 _a978-0-323-85124-4
040 _cSYAMALA BOOK LINKS
082 _2SECOND
_a616.075 ZHO
100 _aZHOU KEVIN S
245 _aDEEP LEARNING FOR MEDICAL IMAGE ANALYSIS
250 _aSECOND EDITION
260 _aCAMBRIDGE
_bELSEVIER INC
_bACADEMIC PRESS
_c2024
300 _aXXIII 518P
_c18X15X2 C.M
365 _b9950
_c
500 _aMEDICAL IMAGE ANALYSIS
520 _aThis book serves as both a textbook and reference for researchers, medical imaging specialists, and AI engineers. It systematically presents how deep learning methods, particularly convolutional neural networks (CNNs), autoencoders, recurrent networks, and generative adversarial networks (GANs), have transformed medical image analysis tasks such as detection, segmentation, classification, and diagnosis.
522 _aCOMPUTER SCIENCE
650 _aMACHINE LEARNING
651 _aCOMPUTER SCIENCE
658 _cR-23
700 _aHAYIT GREENSPAN
700 _aDINGGANG SHEN
760 _bSECOND EDITION
942 _2ddc
_cREF
_e3rd Edition
_n0
999 _c18402
_d18402