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 References
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.. [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

.. [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

.. [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

.. [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.
   https://doi.org/10.1109/TMI.2016.2587062

.. [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

.. [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

.. [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

.. [REFUGE-2018] https://refuge.grand-challenge.org/Details/

.. [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

.. [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

.. [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

.. [LUO-2019] *L. Luo, Y. Xiong, Y. Liu, and X. Sun*, **Adaptive Gradient
   Methods with Dynamic Bound of Learning Rate**, Proceedings of the 7th
   International Conference on Learning Representations (ICLR), Feb. 2019.
   https://arxiv.org/abs/1902.09843v1

.. [MASSA-2018] *F. Massa and R. Girshick*, **maskrcnn-benchmark: Fast, modular
   reference implementation of Instance Segmentation and Object Detection
   algorithms in PyTorch**. 2018.  Last accessed: 21.03.2020.
   https://github.com/facebookresearch/maskrcnn-benchmark

.. [LIN-2018] *J. Lin*, **pytorch-mobilenet-v2: A PyTorch implementation of
   MobileNetV2**, 2018.  Last accessed: 21.03.2020.
   https://github.com/tonylins/pytorch-mobilenet-v2

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

.. [ZHANG-2017] *Z. Zhang, Q. Liu, Y. Wang*, **Road Extraction by Deep Residual
   U-Net**, 2017. https://arxiv.org/abs/1711.10684

.. [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

.. .. [DAVIS-2006] *J. Davis and M. Goadrich*, **The relationship between
   Precision-Recall and ROC curves**. 23rd international conference on Machine
   learning (ICML’06), 2006. https://doi.org/10.1145/1143844.1143874

.. [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

.. [SMITH-2017] *L. N. Smith*, **Cyclical Learning Rates for Training Neural
   Networks**, 2017.  https://arxiv.org/abs/1506.01186

.. [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

.. [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

.. [MC-2014] *S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, G.
   Thoma*, **Two public chest X-ray datasets for computer-aided screening of
   pulmonary diseases.**, Quantitative imaging in medicine and surgery. 2014.
   https://doi:10.3978/j.issn.2223-4292.2014.11.20

.. [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

.. [SHENZHEN-2014] *S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, G.
   Thoma*, **Two public chest X-ray datasets for computer-aided screening of
   pulmonary diseases.**, Quantitative imaging in medicine and surgery. 2014.
   https://doi:10.3978/j.issn.2223-4292.2014.11.20

.. [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