Diagnosis of Takotsubo Syndrome by Robust Feature Selection from the Complex Latent Space of DL-Based Segmentation Network-Reference-Cited by-同舟云学术

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Diagnosis of Takotsubo Syndrome by Robust Feature Selection from the Complex Latent Space of DL-Based Segmentation Network

Published:2024-05-27 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Zaman Fahim Ahmed¹,Alam Wahidul²,Roy Tarun Kanti³,Chang Amanda⁴,Liu Kan⁴,Wu Xiaodong¹

Affiliation:

1. University of Iowa,Department of Electrical and Computer Engineering,IA,USA,52242

2. University of Iowa,Department of Biomedical Engineering,IA,USA,52242

3. University of Iowa,Department of Computer Science,IA,USA,52242

4. University of Iowa,Division of Cardiology,Department of Internal Medicine,IA,USA,52242

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635574.pdf?arnumber=10635574

Reference15 articles.

1. International Expert Consensus Document on Takotsubo Syndrome (Part I): Clinical Characteristics, Diagnostic Criteria, and Pathophysiology

2. Echocardiographic Correlates of Acute Heart Failure, Cardiogenic Shock, and In-Hospital Mortality in Tako-Tsubo Cardiomyopathy

3. Spatio-temporal hybrid neural networks reduce erroneous human “judgement calls” in the diagnosis of Takotsubo syndrome

4. Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

5. Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box

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