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.

[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

[IGLOVIKOV-2018]

V. Iglovikov, S. Seferbekov, A. Buslaev and A. Shvets, TernausNetV2: Fully Convolutional Network for Instance Segmentation, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, 2018, pp. 228-2284. https://doi.org/10.1109/CVPRW.2018.00042

[XIE-2015]

S. Xie and Z. Tu, Holistically-Nested Edge Detection, 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1395-1403. https://doi.org/10.1109/ICCV.2015.164

[MANINIS-2016]

K.-K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool, Deep Retinal Image Understanding, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Cham, 2016, pp. 140–148. https://doi.org/10.1007/978-3-319-46723-8_17

[GALDRAN-2020]

A. Galdran, A. Anjos, J. Dolz, H. Chakor, H. Lombaert, and I. Ben Ayed, The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models, 2020. https://arxiv.org/abs/2009.01907

[JSRT-2000]

J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi, Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules., American Journal of Roentgenology. 2000. https://pubmed.ncbi.nlm.nih.gov/10628457

[CXR8-2017]

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summers, ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases., IEEE CVPR, pp. 3462-3471, 2017. https://arxiv.org/abs/1705.02315

[GAAL-2020]

G. Gaál, B. Maga, A. Lukács, Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation., 2020. https://arxiv.org/abs/2003.10304v1

[DRISHTIGS1-2014]

J. Sivaswamy, S. R. Krishnadas, G. Datt Joshi, M. Jain and A. U. Syed Tabish, Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, 2014, pp. 53-56. https://doi.org/10.1109/ISBI.2014.6867807

[DRIVE-2004]

J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever and B. van Ginneken, Ridge-based vessel segmentation in color images of the retina, in IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, April 2004. https://doi.org/10.1109/TMI.2004.825627

[ORLANDO-2017]

J. I. Orlando, E. Prokofyeva and M. B. Blaschko, A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images, in IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 16-27, Jan. 2017. https://doi.org/10.1109/TBME.2016.2535311

[MEYER-2017]

M. I. Meyer, P. Costa, A. Galdran, A. M. Mendonça, and A. Campilho, A Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Images, in Image Analysis and Recognition, vol. 10317, F. Karray, A. Campilho, and F. Cheriet, Eds. Cham: Springer International Publishing, 2017, pp. 507–515. https://doi.org/10.1007/978-3-319-59876-5_56

[REFUGE-2018]

https://refuge.grand-challenge.org/Details/

[CHASEDB1-2012]

M. M. Fraz et al., An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation, in IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2538-2548, Sept. 2012. https://doi.org/10.1109/TBME.2012.2205687

[DRIONSDB-2008]

Enrique J. Carmona, Mariano Rincón, Julián García-Feijoó, José M. Martínez-de-la-Casa, Identification of the optic nerve head with genetic algorithms, in Artificial Intelligence in Medicine, Volume 43, Issue 3, pp. 243-259, 2008. http://dx.doi.org/10.1016/j.artmed.2008.04.005

[HRF-2013]

A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, Robust Vessel Segmentation in Fundus Images, in International Journal of Biomedical Imaging, vol. 2013, p. 11, 2013. http://dx.doi.org/10.1155/2013/154860

[IOSTAR-2016]

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits and B. M. ter Haar Romeny, Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores, in IEEE Transactions on Medical Imaging, vol. 35, no. 12, pp. 2631-2644, Dec. 2016.

[RIMONER3-2015]

F. Fumero, J. Sigut, S. Alayón, M. González-Hernández, M. González de la Rosa, Interactive Tool and Database for Optic Disc and Cup Segmentation of Stereo and Monocular Retinal Fundus Images, Conference on Computer Graphics, Visualization and Computer Vision, 2015. https://dspace5.zcu.cz/bitstream/11025/29670/1/Fumero.pdf

[STARE-2000]

A. D. Hoover, V. Kouznetsova and M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, in IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203-210, March 2000. https://doi.org/10.1109/42.845178

[SANDLER-2018]

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C.h Chen, MobileNetV2: Inverted Residuals and Linear Bottlenecks, 2018. https://arxiv.org/abs/1801.04381

[RONNEBERGER-2015]

O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015. https://arxiv.org/abs/1505.04597

[DRHAGIS-2017]

S. Holm, G. Russell, V. Nourrit, N. McLoughlin, DR HAGIS – A Novel Fundus Image Database for the Automatic Extraction of Retinal Surface Vessels, SPIE Journal of Medical Imaging, 2017. https://doi.org/10.1117/1.jmi.4.1.014503

[VISCERAL-2016]

O. Jimenez-del-Toro et al., Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks, IEEE Transactions on Medical Imaging, vol. 35, no. 11, pp. 2459-2475, Nov. 2016, https://doi.org/10.1109/TMI.2016.2578680

[ALEXNET-2012]

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems (NIPS) 25, 2012. https://doi.org/10.1145/3065386

[DENSENET-2017]

G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, Densely Connected Convolutional Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.243.

[LAIBACHER-2018]

Tim Laibacher, Tillman Weyde, Sepehr Jalali, M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments, 2018. https://doi.org/10.48550/arXiv.1811.07738