The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession
• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kortesniemi, Mika [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Umfang: |
4 |
|---|
| Übergeordnetes Werk: |
Enthalten in: An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study - Almanassra, Ismail W. ELSEVIER, 2019, European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:56 ; year:2018 ; pages:90-93 ; extent:4 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ejmp.2018.11.005 |
|---|
| Katalog-ID: |
ELV045152179 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV045152179 | ||
| 003 | DE-627 | ||
| 005 | 20230624113816.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 190205s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ejmp.2018.11.005 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000448.pica |
| 035 | |a (DE-627)ELV045152179 | ||
| 035 | |a (ELSEVIER)S1120-1797(18)31315-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |q VZ |
| 084 | |a 35.21 |2 bkl | ||
| 100 | 1 | |a Kortesniemi, Mika |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| 264 | 1 | |c 2018 | |
| 300 | |a 4 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. | ||
| 700 | 1 | |a Tsapaki, Virginia |4 oth | |
| 700 | 1 | |a Trianni, Annalisa |4 oth | |
| 700 | 1 | |a Russo, Paolo |4 oth | |
| 700 | 1 | |a Maas, Ad |4 oth | |
| 700 | 1 | |a Källman, Hans-Erik |4 oth | |
| 700 | 1 | |a Brambilla, Marco |4 oth | |
| 700 | 1 | |a Damilakis, John |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Almanassra, Ismail W. ELSEVIER |t An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |d 2019 |d European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology |g Amsterdam |w (DE-627)ELV003857336 |
| 773 | 1 | 8 | |g volume:56 |g year:2018 |g pages:90-93 |g extent:4 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.ejmp.2018.11.005 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.21 |j Lösungen |j Flüssigkeiten |x Physikalische Chemie |q VZ |
| 951 | |a AR | ||
| 952 | |d 56 |j 2018 |h 90-93 |g 4 | ||
| author_variant |
m k mk |
|---|---|
| matchkey_str |
kortesniemimikatsapakivirginiatrianniann:2018----:herpafdrtooognstosomdclhsceophtppridtadeperigneiaiaign |
| hierarchy_sort_str |
2018 |
| bklnumber |
35.21 |
| publishDate |
2018 |
| allfields |
10.1016/j.ejmp.2018.11.005 doi GBV00000000000448.pica (DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 DE-627 ger DE-627 rakwb eng 540 VZ 35.21 bkl Kortesniemi, Mika verfasserin aut The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession 2018 4 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. Tsapaki, Virginia oth Trianni, Annalisa oth Russo, Paolo oth Maas, Ad oth Källman, Hans-Erik oth Brambilla, Marco oth Damilakis, John oth Enthalten in Elsevier Almanassra, Ismail W. ELSEVIER An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study 2019 European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology Amsterdam (DE-627)ELV003857336 volume:56 year:2018 pages:90-93 extent:4 https://doi.org/10.1016/j.ejmp.2018.11.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.21 Lösungen Flüssigkeiten Physikalische Chemie VZ AR 56 2018 90-93 4 |
| spelling |
10.1016/j.ejmp.2018.11.005 doi GBV00000000000448.pica (DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 DE-627 ger DE-627 rakwb eng 540 VZ 35.21 bkl Kortesniemi, Mika verfasserin aut The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession 2018 4 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. Tsapaki, Virginia oth Trianni, Annalisa oth Russo, Paolo oth Maas, Ad oth Källman, Hans-Erik oth Brambilla, Marco oth Damilakis, John oth Enthalten in Elsevier Almanassra, Ismail W. ELSEVIER An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study 2019 European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology Amsterdam (DE-627)ELV003857336 volume:56 year:2018 pages:90-93 extent:4 https://doi.org/10.1016/j.ejmp.2018.11.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.21 Lösungen Flüssigkeiten Physikalische Chemie VZ AR 56 2018 90-93 4 |
| allfields_unstemmed |
10.1016/j.ejmp.2018.11.005 doi GBV00000000000448.pica (DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 DE-627 ger DE-627 rakwb eng 540 VZ 35.21 bkl Kortesniemi, Mika verfasserin aut The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession 2018 4 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. Tsapaki, Virginia oth Trianni, Annalisa oth Russo, Paolo oth Maas, Ad oth Källman, Hans-Erik oth Brambilla, Marco oth Damilakis, John oth Enthalten in Elsevier Almanassra, Ismail W. ELSEVIER An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study 2019 European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology Amsterdam (DE-627)ELV003857336 volume:56 year:2018 pages:90-93 extent:4 https://doi.org/10.1016/j.ejmp.2018.11.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.21 Lösungen Flüssigkeiten Physikalische Chemie VZ AR 56 2018 90-93 4 |
| allfieldsGer |
10.1016/j.ejmp.2018.11.005 doi GBV00000000000448.pica (DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 DE-627 ger DE-627 rakwb eng 540 VZ 35.21 bkl Kortesniemi, Mika verfasserin aut The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession 2018 4 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. Tsapaki, Virginia oth Trianni, Annalisa oth Russo, Paolo oth Maas, Ad oth Källman, Hans-Erik oth Brambilla, Marco oth Damilakis, John oth Enthalten in Elsevier Almanassra, Ismail W. ELSEVIER An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study 2019 European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology Amsterdam (DE-627)ELV003857336 volume:56 year:2018 pages:90-93 extent:4 https://doi.org/10.1016/j.ejmp.2018.11.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.21 Lösungen Flüssigkeiten Physikalische Chemie VZ AR 56 2018 90-93 4 |
| allfieldsSound |
10.1016/j.ejmp.2018.11.005 doi GBV00000000000448.pica (DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 DE-627 ger DE-627 rakwb eng 540 VZ 35.21 bkl Kortesniemi, Mika verfasserin aut The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession 2018 4 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. Tsapaki, Virginia oth Trianni, Annalisa oth Russo, Paolo oth Maas, Ad oth Källman, Hans-Erik oth Brambilla, Marco oth Damilakis, John oth Enthalten in Elsevier Almanassra, Ismail W. ELSEVIER An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study 2019 European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology Amsterdam (DE-627)ELV003857336 volume:56 year:2018 pages:90-93 extent:4 https://doi.org/10.1016/j.ejmp.2018.11.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.21 Lösungen Flüssigkeiten Physikalische Chemie VZ AR 56 2018 90-93 4 |
| language |
English |
| source |
Enthalten in An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study Amsterdam volume:56 year:2018 pages:90-93 extent:4 |
| sourceStr |
Enthalten in An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study Amsterdam volume:56 year:2018 pages:90-93 extent:4 |
| format_phy_str_mv |
Article |
| bklname |
Lösungen Flüssigkeiten |
| institution |
findex.gbv.de |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |
| authorswithroles_txt_mv |
Kortesniemi, Mika @@aut@@ Tsapaki, Virginia @@oth@@ Trianni, Annalisa @@oth@@ Russo, Paolo @@oth@@ Maas, Ad @@oth@@ Källman, Hans-Erik @@oth@@ Brambilla, Marco @@oth@@ Damilakis, John @@oth@@ |
| publishDateDaySort_date |
2018-01-01T00:00:00Z |
| hierarchy_top_id |
ELV003857336 |
| dewey-sort |
3540 |
| id |
ELV045152179 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045152179</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624113816.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ejmp.2018.11.005</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000448.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045152179</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1120-1797(18)31315-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.21</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kortesniemi, Mika</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">4</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsapaki, Virginia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trianni, Annalisa</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Russo, Paolo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Maas, Ad</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Källman, Hans-Erik</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brambilla, Marco</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Damilakis, John</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Almanassra, Ismail W. ELSEVIER</subfield><subfield code="t">An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study</subfield><subfield code="d">2019</subfield><subfield code="d">European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV003857336</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:56</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:90-93</subfield><subfield code="g">extent:4</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ejmp.2018.11.005</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.21</subfield><subfield code="j">Lösungen</subfield><subfield code="j">Flüssigkeiten</subfield><subfield code="x">Physikalische Chemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">56</subfield><subfield code="j">2018</subfield><subfield code="h">90-93</subfield><subfield code="g">4</subfield></datafield></record></collection>
|
| author |
Kortesniemi, Mika |
| spellingShingle |
Kortesniemi, Mika ddc 540 bkl 35.21 The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| authorStr |
Kortesniemi, Mika |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV003857336 |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
540 VZ 35.21 bkl The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| topic |
ddc 540 bkl 35.21 |
| topic_unstemmed |
ddc 540 bkl 35.21 |
| topic_browse |
ddc 540 bkl 35.21 |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
v t vt a t at p r pr a m am h e k hek m b mb j d jd |
| hierarchy_parent_title |
An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |
| hierarchy_parent_id |
ELV003857336 |
| dewey-tens |
540 - Chemistry |
| hierarchy_top_title |
An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV003857336 |
| title |
The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| ctrlnum |
(DE-627)ELV045152179 (ELSEVIER)S1120-1797(18)31315-2 |
| title_full |
The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| author_sort |
Kortesniemi, Mika |
| journal |
An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |
| journalStr |
An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
zzz |
| container_start_page |
90 |
| author_browse |
Kortesniemi, Mika |
| container_volume |
56 |
| physical |
4 |
| class |
540 VZ 35.21 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Kortesniemi, Mika |
| doi_str_mv |
10.1016/j.ejmp.2018.11.005 |
| dewey-full |
540 |
| title_sort |
european federation of organisations for medical physics (efomp) white paper: big data and deep learning in medical imaging and in relation to medical physics profession |
| title_auth |
The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| abstract |
• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. |
| abstractGer |
• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. |
| abstract_unstemmed |
• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession |
| url |
https://doi.org/10.1016/j.ejmp.2018.11.005 |
| remote_bool |
true |
| author2 |
Tsapaki, Virginia Trianni, Annalisa Russo, Paolo Maas, Ad Källman, Hans-Erik Brambilla, Marco Damilakis, John |
| author2Str |
Tsapaki, Virginia Trianni, Annalisa Russo, Paolo Maas, Ad Källman, Hans-Erik Brambilla, Marco Damilakis, John |
| ppnlink |
ELV003857336 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth oth |
| doi_str |
10.1016/j.ejmp.2018.11.005 |
| up_date |
2024-07-06T16:45:14.988Z |
| _version_ |
1803848845877051392 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045152179</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624113816.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ejmp.2018.11.005</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000448.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045152179</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1120-1797(18)31315-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.21</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kortesniemi, Mika</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">4</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Artificial intelligence is profoundly changing professions, applications and research. • Emerging AI methods may enable more comprehensive optimisation, dosimetry and QA. • Challenges in data utilisation involve access, privacy, labeling and validation. • Technological transform has also potential impact on our multidisciplinary role. • Our professional role and education should keep up with the development of AI methods.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsapaki, Virginia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trianni, Annalisa</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Russo, Paolo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Maas, Ad</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Källman, Hans-Erik</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brambilla, Marco</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Damilakis, John</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Almanassra, Ismail W. ELSEVIER</subfield><subfield code="t">An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study</subfield><subfield code="d">2019</subfield><subfield code="d">European journal of medical physics : an international journal devoted to the applications of physics to medicine and biology</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV003857336</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:56</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:90-93</subfield><subfield code="g">extent:4</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ejmp.2018.11.005</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.21</subfield><subfield code="j">Lösungen</subfield><subfield code="j">Flüssigkeiten</subfield><subfield code="x">Physikalische Chemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">56</subfield><subfield code="j">2018</subfield><subfield code="h">90-93</subfield><subfield code="g">4</subfield></datafield></record></collection>
|
| score |
7.400923 |