.. SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch> .. .. SPDX-License-Identifier: GPL-3.0-or-later ============ 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