References

MONTGOMERY-SHENZHEN-2014

Jaeger S, Candemir S, Antani S, Wáng YX, Lu PX, Thoma G., Two public chest X-ray datasets for computer-aided screening of pulmonary diseases., Quant Imaging Med Surg. 2014;4(6):475‐477. https://dx.doi.org/10.3978%2Fj.issn.2223-4292.2014.11.20

INDIAN-2013

https://sourceforge.net/projects/tbxpredict/

PASA-2019

Pasa, F., Golkov, V., Pfeiffer, F. et al., Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. Sci Rep 9, 6268 (2019). https://doi.org/10.1038/s41598-019-42557-4

SIMARD-2003

P. Y. Simard, D. Steinkraus and J. C. Platt, Best practices for convolutional neural networks applied to visual document analysis, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., Edinburgh, UK, 2003, pp. 958-963. https://doi.org/10.1109/ICDAR.2003.1227801

CHEXNEXT-2018

Rajpurkar Pranav, Jeremy Irvin, Robyn L. Ball, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, et al., Deep Learning for Chest Radiograph Diagnosis: A Retrospective Comparison of the CheXNeXt Algorithm to Practicing Radiologists. PLOS Medicine 15, nᵒ 11 (20 november 2018): e1002686. https://doi.org/10.1371/journal.pmed.1002686

NIH-CXR14-2017

Xiaosong Wang et al., ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, July 2017, pp. 3462–3471. doi: 10.1109/CVPR.2017.369. http://ieeexplore.ieee.org/document/8099852/

PADCHEST-2019

Aurelia Bustos et al., PadChest: A large chest x-ray image dataset with multi-label annotated reports Medical Image Analysis, Volume 66, 2020, 101797, ISSN 1361-8415. doi: 10.1016/j.media.2020.101797. https://www.sciencedirect.com/science/article/abs/pii/S1361841520301614

GOUTTE-2005

C. Goutte and E. Gaussier, A probabilistic interpretation of precision, recall and F-score, with implication for evaluation, European conference on Advances in Information Retrieval Research, 2005. https://doi.org/10.1007/978-3-540-31865-1_25

TB-POC-2018

Griesel, Rulan and Stewart, Annemie and van der Plas, Helen and Sikhondze, Welile and Rangaka, Molebogeng X and Nicol, Mark P and Kengne, Andre P and Mendelson, Marc and Maartens, Gary, Optimizing Tuberculosis Diagnosis in Human Immunodeficiency Virus–Infected Inpatients Meeting the Criteria of Seriously Ill in the World Health Organization Algorithm, Clinical Infectious Diseases, 2017. https://doi.org/10.1093/cid/cix988

HIV-TB-2019

Van Hoving, D. J. et al., Brief report: real-world performance and interobserver agreement of urine lipoarabinomannan in diagnosing HIV-Associated tuberculosis in an emergency center., J. Acquir. Immune Defic. Syndr. 1999 81, e10–e14 (2019).