東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Deep Learning for Medical Image Interpretation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Medical Image Interpretation./
Author:	Rajpurkar, Pranav Samir.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	208 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Metadata. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688399
ISBN:	9798544204688

Deep Learning for Medical Image Interpretation.
Rajpurkar, Pranav Samir.

Deep Learning for Medical Image Interpretation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 208 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

There have been rapid advances at the intersection of deep learning and medicine over the last few years, especially for the interpretation of medical images. In this thesis, I describe three key directions that present challenges and opportunities for the development of deep learning technologies for medical image interpretation. First, I discuss the development of algorithms for expert-level medical image interpretation, with a focus on transfer learning and self-supervised learning algorithms designed to work in low labeled medical data settings. Second, I discuss the design and curation of high-quality datasets and their roles in advancing algorithmic developments, with a focus on high-quality labeling with limited manual annotations. Third, I discuss the real-world evaluation of medical image algorithms with studies systematically analyzing performance under clinically relevant distribution shifts. Altogether this thesis summarizes key contributions and insights in each of these directions with key applications across medical specialties.

ISBN: 9798544204688Subjects--Topical Terms:

590006
Metadata.

Deep Learning for Medical Image Interpretation.
LDR:02043nmm a2200301 4500 001 2349849
005 20221010063638.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544204688
035 $a (MiAaPQ)AAI28688399
035 $a (MiAaPQ)STANFORDjc097kx0188
035 $a AAI28688399
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rajpurkar, Pranav Samir. $3 3689272
245 1 0 $a Deep Learning for Medical Image Interpretation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 208 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Bernstein, Michael;Liang, Percy.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a There have been rapid advances at the intersection of deep learning and medicine over the last few years, especially for the interpretation of medical images. In this thesis, I describe three key directions that present challenges and opportunities for the development of deep learning technologies for medical image interpretation. First, I discuss the development of algorithms for expert-level medical image interpretation, with a focus on transfer learning and self-supervised learning algorithms designed to work in low labeled medical data settings. Second, I discuss the design and curation of high-quality datasets and their roles in advancing algorithmic developments, with a focus on high-quality labeling with limited manual annotations. Third, I discuss the real-world evaluation of medical image algorithms with studies systematically analyzing performance under clinically relevant distribution shifts. Altogether this thesis summarizes key contributions and insights in each of these directions with key applications across medical specialties.
590 $a School code: 0212.
650 4 $a Metadata. $3 590006
650 4 $a Deep learning. $3 3554982
650 4 $a X rays. $3 3682558
650 4 $a Artificial intelligence. $3 516317
690 $a 0800
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688399