| LDR | | 01789nam u200397 4500 |
| 001 | | 000000418990 |
| 005 | | 20190215163321 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438122376 |
| 035 | |
▼a (MiAaPQ)AAI10902869 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Xing, Fuyong. |
| 245 | 10 |
▼a High-Throughput Biomedical Image Computing for Digital Health. |
| 260 | |
▼a [S.l.]:
▼b University of Florida.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 114 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Adviser: Lin Yang. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Florida, 2017. |
| 520 | |
▼a In biomedical informatics, a large amount of image data has been collected to support clinical diagnosis, treatment decision, and medical prognosis. The large volume and the diversity of informatics across different imaging modalities require ad |
| 520 | |
▼a In this dissertation, we will describe a high-throughput biomedical image computing framework for digital health, focusing on two important topics: object detection and segmentation as well as their applications, image understanding, in medical |
| 590 | |
▼a School code: 0070. |
| 650 | 4 |
▼a Computer engineering. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0464 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Florida. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0070 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000422
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |