Assisting Radiologists in X-Ray Diagnostics

Author:

Avramescu CristianORCID,Bogdan BerceanORCID,Iarca StefanORCID,Tenescu AndreiORCID,Fuicu SebastianORCID

Publisher

Springer International Publishing

Reference22 articles.

1. PAHO WHO. World Radiography Day: Two-Thirds of the World’s Population has no Access to Diagnostic Imaging. PAHO (2012)

2. Stec, N., Arje, D., Moody, A.R., Krupinski, E.A., Tyrrell, P.N.: A systematic review of fatigue in radiology: is it a problem? AJR Am. J. Roentgenol. 210(4), 799–806 (2018)

3. Rajpurkar, P., et al.: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv e-prints, art. arXiv:1711.05225 , November 2017

4. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv e-prints, art. arXiv:1710.10501 , October 2017

5. Kumar, P., Grewal, M., Srivastava, M.M.: Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs. arXiv e-prints, art. arXiv:1711.08760 , November 2017

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2. A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning;Mathematics;2022-12-15

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