1.

Record Nr.

UNINA9910705123703321

Autore

Maurer David C.

Titolo

Department of Homeland Security : DHS's efforts to improve employee morale and fill senior leadership vacancies : testimony before the Committee on Homeland Security, House of Representatives / / statement of David C. Maurer

Pubbl/distr/stampa

[Washington, D.C.] : , : United States Government Accountability Office, , 2013

Descrizione fisica

1 online resource (17 pages) : color illustrations

Collana

Testimony ; ; GAO-14-228T

Soggetti

Employee morale - United States

Government executives - Selection and appointment

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Title from title screen (viewed Feb. 5, 2014).

"For release ... December 12, 2013."

Nota di bibliografia

Includes bibliographical references.



2.

Record Nr.

UNINA9910484925303321

Titolo

Machine Learning in Medical Imaging : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016

ISBN

3-319-47157-0

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (XIV, 324 p. 127 illus.)

Collana

Image Processing, Computer Vision, Pattern Recognition, and Graphics, , 3004-9954 ; ; 10019

Disciplina

006.6

Soggetti

Computer vision

Pattern recognition systems

Medical informatics

Data mining

Artificial intelligence

Computer Vision

Automated Pattern Recognition

Health Informatics

Data Mining and Knowledge Discovery

Artificial Intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Sommario/riassunto

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new



cutting-edge techniques and their use in medical imaging.