references.rst

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

[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).

[TBX11K-2020]

Liu, Y., Wu, Y.-H., Ban, Y., Wang, H., and Cheng, M.-, Rethinking computer-aided tuberculosis diagnosis., In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2646–2655.

[TBX11K-SIMPLIFIED-2020]

Liu, Y., Wu, Y.-H., Ban, Y., Wang, H., and Cheng, M.-, Rethinking computer-aided tuberculosis diagnosis, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2646–2655.

[SCORECAM-2020]

H. Wang et al., Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020 pp. 111-119. doi: https://doi.org/10.1109/CVPRW50498.2020.00020

[ROAD-2022]

Y. Rong, T. Leemann, V. Borisov, G. Kasneci, and E. Kasneci, A Consistent and Efficient Evaluation Strategy for Attribution Methods in Proceedings of the 39th International Conference on Machine Learning, PMLR, Jun. 2022, pp. 18770–18795. https://proceedings.mlr.press/v162/rong22a.html