TY - GEN
T1 - Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19
T2 - 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
AU - Gabrani, Maria
AU - Konukoglu, Ender
AU - Beymer, David
AU - Carneiro, Gustavo
AU - Born, Jannis
AU - Guindy, Michal
AU - Rosen-Zvi, Michal
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - The global COVID-19 pandemic has resulted in huge pressures on healthcare systems, with lung imaging, from chest radiographs (CXR) to computed tomography (CT) and ultrasound (US) of the thorax, playing an important role in the diagnosis and management of patients with coronavirus infection. The AI community reacted rapidly to the threat of the coronavirus pandemic by contributing numerous initiatives of developing AI technologies for interpreting lung images across the different modalities. We performed a thorough review of all relevant publications in 2020 [1] and identified numerous trends and insights that may help in accelerating the translation of AI technology in clinical practice in pandemic times. This workshop is devoted to the lessons learned from this accelerated process and in paving the way for further AI adoption. In particular, the objective is to bring together radiologists and AI experts to review the scientific progress in the development of AI technologies for medical imaging to address the COVID-19 pandemic and share observations regarding the data relevance, the data availability and the translational aspects of AI research and development. We aim at understanding if and what needs to be done differently in developing technologies of AI for lung images of COVID-19 patients, given the pressure of an unprecedented pandemic - which processes are working, which should be further adapted, and which approaches should be abandoned.
AB - The global COVID-19 pandemic has resulted in huge pressures on healthcare systems, with lung imaging, from chest radiographs (CXR) to computed tomography (CT) and ultrasound (US) of the thorax, playing an important role in the diagnosis and management of patients with coronavirus infection. The AI community reacted rapidly to the threat of the coronavirus pandemic by contributing numerous initiatives of developing AI technologies for interpreting lung images across the different modalities. We performed a thorough review of all relevant publications in 2020 [1] and identified numerous trends and insights that may help in accelerating the translation of AI technology in clinical practice in pandemic times. This workshop is devoted to the lessons learned from this accelerated process and in paving the way for further AI adoption. In particular, the objective is to bring together radiologists and AI experts to review the scientific progress in the development of AI technologies for medical imaging to address the COVID-19 pandemic and share observations regarding the data relevance, the data availability and the translational aspects of AI research and development. We aim at understanding if and what needs to be done differently in developing technologies of AI for lung images of COVID-19 patients, given the pressure of an unprecedented pandemic - which processes are working, which should be further adapted, and which approaches should be abandoned.
KW - AI
KW - COVID-19
KW - CT
KW - CXR/XRay
KW - Medical lung imaging
KW - Ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85120681016&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90874-4_13
DO - 10.1007/978-3-030-90874-4_13
M3 - Conference contribution
AN - SCOPUS:85120681016
SN - 9783030908737
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 140
BT - Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Oyarzun Laura, Cristina
A2 - Cardoso, M. Jorge
A2 - Rosen-Zvi, Michal
A2 - Kaissis, Georgios
A2 - Linguraru, Marius George
A2 - Shekhar, Raj
A2 - Wesarg, Stefan
A2 - Erdt, Marius
A2 - Drechsler, Klaus
A2 - Chen, Yufei
A2 - Albarqouni, Shadi
A2 - Bakas, Spyridon
A2 - Landman, Bennett
A2 - Rieke, Nicola
A2 - Roth, Holger
A2 - Li, Xiaoxiao
A2 - Xu, Daguang
A2 - Gabrani, Maria
A2 - Konukoglu, Ender
A2 - Guindy, Michal
A2 - Rueckert, Daniel
A2 - Ziller, Alexander
A2 - Usynin, Dmitrii
A2 - Passerat-Palmbach, Jonathan
A2 - Oyarzun Laura, Cristina
A2 - Cardoso, M. Jorge
A2 - Rosen-Zvi, Michal
A2 - Kaissis, Georgios
A2 - Linguraru, Marius George
A2 - Shekhar, Raj
A2 - Wesarg, Stefan
A2 - Erdt, Marius
A2 - Drechsler, Klaus
A2 - Chen, Yufei
A2 - Albarqouni, Shadi
A2 - Bakas, Spyridon
A2 - Landman, Bennett
A2 - Rieke, Nicola
A2 - Roth, Holger
A2 - Li, Xiaoxiao
A2 - Xu, Daguang
A2 - Gabrani, Maria
A2 - Konukoglu, Ender
A2 - Guindy, Michal
A2 - Rueckert, Daniel
A2 - Ziller, Alexander
A2 - Usynin, Dmitrii
A2 - Passerat-Palmbach, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 September 2021 through 1 October 2021
ER -
