Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
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DOI: 10.1371/journal.pmed.1002686
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- Oded Rotem & Tamar Schwartz & Ron Maor & Yishay Tauber & Maya Tsarfati Shapiro & Marcos Meseguer & Daniella Gilboa & Daniel S. Seidman & Assaf Zaritsky, 2024. "Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
- Seung Seog Han & Ik Jun Moon & Seong Hwan Kim & Jung-Im Na & Myoung Shin Kim & Gyeong Hun Park & Ilwoo Park & Keewon Kim & Woohyung Lim & Ju Hee Lee & Sung Eun Chang, 2020. "Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study," PLOS Medicine, Public Library of Science, vol. 17(11), pages 1-21, November.
- Weijie Fan & Yi Yang & Jing Qi & Qichuan Zhang & Cuiwei Liao & Li Wen & Shuang Wang & Guangxian Wang & Yu Xia & Qihua Wu & Xiaotao Fan & Xingcai Chen & Mi He & JingJing Xiao & Liu Yang & Yun Liu & Jia, 2024. "A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Shashank Shetty & Ananthanarayana V S. & Ajit Mahale, 2022. "MS-CheXNet: An Explainable and Lightweight Multi-Scale Dilated Network with Depthwise Separable Convolution for Prediction of Pulmonary Abnormalities in Chest Radiographs," Mathematics, MDPI, vol. 10(19), pages 1-29, October.
- Eric Engle & Andrei Gabrielian & Alyssa Long & Darrell E Hurt & Alex Rosenthal, 2020. "Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
- Mingzhu Liu & Chirag Nagpal & Artur Dubrawski, 2024. "Deep Survival Models Can Improve Long-Term Mortality Risk Estimates from Chest Radiographs," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
- Eun Young Kim & Young Jae Kim & Won-Jun Choi & Gi Pyo Lee & Ye Ra Choi & Kwang Nam Jin & Young Jun Cho, 2021. "Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-12, February.
- Tianyu Han & Sven Nebelung & Federico Pedersoli & Markus Zimmermann & Maximilian Schulze-Hagen & Michael Ho & Christoph Haarburger & Fabian Kiessling & Christiane Kuhl & Volkmar Schulz & Daniel Truhn, 2021. "Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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