Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning-Reference-Cited by-同舟云学术

Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning

Published:2024-09-01 Issue:5 Volume:6 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Yu Ke¹^ORCID,Ghosh Shantanu¹^ORCID,Liu Zhexiong¹^ORCID,Deible Christopher¹,Poynton Clare B.¹^ORCID,Batmanghelich Kayhan¹^ORCID

Affiliation:

1. From the School of Computing and Information, University of Pittsburgh, Pittsburgh, Pa (K.Y., Z.L.); Department of Electrical and Computer Engineering, Boston University, 8 St. Mary’s St, Office 421, Boston, MA 02215 (S.G., K.B.); Department of Radiology, University of Pittsburgh, Pittsburgh, Pa (C.D.); and Chobanian & Avedisian School of Medicine, Boston University, Boston, Mass (C.B.P.).

Funder

Pennsylvania Department of Health

National Institutes of Health

National Science Foundation

Publisher

Radiological Society of North America (RSNA)

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.230277

Reference22 articles.

1. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society

2. Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks

3. Tension pneumothorax--time for a re-think?

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