| 000 | 04828cam a22007338i 4500 | ||
|---|---|---|---|
| 999 |
_c96316 _d96316 |
||
| 001 | 14321005 | ||
| 005 | 20260801084123.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 250106s2025 nju o 000 0 eng d | ||
| 015 |
_aGBC506791 _2bnb |
||
| 016 | 7 |
_a300482739 _2Uk |
|
| 020 | _a9781394245338 | ||
| 020 | _a1394245343 | ||
| 020 |
_a9781394245369 _q(electronic bk.) |
||
| 020 |
_a139424536X _q(electronic bk.) |
||
| 020 |
_a9781394245352 _q(electronic bk.) |
||
| 020 |
_a1394245351 _q(electronic bk.) |
||
| 020 |
_a9781394245345 _q(electronic bk.) |
||
| 024 | 8 | _aCIPO000187921 | |
| 024 | 7 |
_a10.1002/9781394245369 _2doi |
|
| 035 |
_a(OCoLC)1518017491 _z(OCoLC)1482905420 |
||
| 035 | 9 | _a(OCLCCM-Owned)1518017491 | |
| 037 |
_a10833909 _bIEEE |
||
| 037 |
_aaxeusr89 _bcloudLibrary _nhttps://yourcloudlibrary.com |
||
| 040 |
_aUKMGB _beng _erda _epn _cUKMGB _dOCLCO _dCLOUD _dEBLCP _dIEEEE _dYDX _dDG1 _dUKAHL _dHOPLA _dN$T |
||
| 041 | _aeng | ||
| 049 | _aMAIN | ||
| 050 | 4 |
_aR857.O6 _bD44 2025 |
|
| 072 | 7 |
_aCOM012050 _2bisacsh/2023 |
|
| 072 | 7 |
_aTEC059000 _2bisacsh/2023 |
|
| 072 | 7 |
_aTEC000000 _2bisacsh/2023 |
|
| 072 | 7 |
_aCOM000000 _2bisacsh/2023 |
|
| 245 | 0 | 0 |
_aDeep learning applications in medical image segmentation : _boverview, approaches, and challenges / _cedited by Sajid Yousuf Bhat, Aasia Rehman, Muhammad Abulaish. |
| 264 | 1 |
_aHoboken : _bWiley-IEEE Press, _c2025. |
|
| 300 | _a1 online resource | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 520 | _aApply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many more Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation Identifies and discusses the key challenges faced in medical image segmentation using deep learning techniques Provides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering. | ||
| 588 | _aDescription based on CIP data; resource not viewed. | ||
| 650 | 0 |
_aDiagnostic imaging _xData processing. |
|
| 650 | 0 |
_aImage segmentation _xTherapeutic use. |
|
| 650 | 0 |
_aDeep learning (Machine learning) _xTherapeutic use. |
|
| 650 | 2 |
_aDeep Learning _0https://id.nlm.nih.gov/mesh/D000077321 |
|
| 650 | 6 |
_aImagerie pour le diagnostic _xInformatique. |
|
| 650 | 6 |
_aSegmentation d'image _xEmploi en thérapeutique. |
|
| 650 | 6 |
_aApprentissage profond _xEmploi en thérapeutique. |
|
| 650 | 7 |
_aImage Processing. _2bisacsh/2023 |
|
| 650 | 7 |
_aBiomedical. _2bisacsh/2023 |
|
| 650 | 7 |
_aTECHNOLOGY & ENGINEERING. _2bisacsh/2023 |
|
| 650 | 7 |
_aCOMPUTERS. _2bisacsh/2023 |
|
| 650 | 7 |
_aImage Processing. _2bisacsh/2024 |
|
| 650 | 7 |
_aBiomedical. _2bisacsh/2024 |
|
| 650 | 7 |
_aTECHNOLOGY & ENGINEERING. _2bisacsh/2024 |
|
| 650 | 7 |
_aCOMPUTERS. _2bisacsh/2024 |
|
| 655 | 0 | _aElectronic books. | |
| 700 | 1 |
_aBhat, Sajid Yousuf, _eeditor. |
|
| 700 | 1 |
_aRehman, Aasia, _eeditor. |
|
| 700 | 1 |
_aAbulaish, Muhammad, _eeditor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781394245338 |
| 856 | 4 | 0 |
_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394245369 _yFull text is available at Wiley Online Library. Click here to view. |
| 942 |
_2ddc _cER |
||