Update paper.bib

paper/paper.bib

@@ -56,3 +56,73 @@ @article{zhou2022
   publisher={The Association for Research in Vision and Ophthalmology},
   url={https://tvst.arvojournals.org/article.aspx?articleid=2783477}
 }
+
+@article{adam,
+   title={ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images},
+   volume={41},
+   ISSN={1558-254X},
+   url={http://dx.doi.org/10.1109/TMI.2022.3172773},
+   DOI={10.1109/tmi.2022.3172773},
+   number={10},
+   journal={IEEE Transactions on Medical Imaging},
+   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
+   author={Fang, Huihui and Li, Fei and Fu, Huazhu and Sun, Xu and Cao, Xingxing and Lin, Fengbin and Son, Jaemin and Kim, Sunho and Quellec, Gwenole and Matta, Sarah and Shankaranarayana, Sharath M. and Chen, Yi-Ting and Wang, Chuen-Heng and Shah, Nisarg A. and Lee, Chia-Yen and Hsu, Chih-Chung and Xie, Hai and Lei, Baiying and Baid, Ujjwal and Innani, Shubham and Dang, Kang and Shi, Wenxiu and Kamble, Ravi and Singhal, Nitin and Wang, Ching-Wei and Lo, Shih-Chang and Orlando, Jose Ignacio and Bogunovic, Hrvoje and Zhang, Xiulan and Xu, Yanwu},
+   year={2022},
+   month=oct, pages={2828–2847} }
+
+@article{refuge,
+   title={REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs},
+   volume={59},
+   ISSN={1361-8415},
+   url={http://dx.doi.org/10.1016/j.media.2019.101570},
+   DOI={10.1016/j.media.2019.101570},
+   journal={Medical Image Analysis},
+   publisher={Elsevier BV},
+   author={Orlando, José Ignacio and Fu, Huazhu and Barbosa Breda, João and van Keer, Karel and Bathula, Deepti R. and Diaz-Pinto, Andrés and Fang, Ruogu and Heng, Pheng-Ann and Kim, Jeyoung and Lee, JoonHo and Lee, Joonseok and Li, Xiaoxiao and Liu, Peng and Lu, Shuai and Murugesan, Balamurali and Naranjo, Valery and Phaye, Sai Samarth R. and Shankaranarayana, Sharath M. and Sikka, Apoorva and Son, Jaemin and van den Hengel, Anton and Wang, Shujun and Wu, Junyan and Wu, Zifeng and Xu, Guanghui and Xu, Yongli and Yin, Pengshuai and Li, Fei and Zhang, Xiulan and Xu, Yanwu and Bogunović, Hrvoje},
+   year={2020},
+   month=jan, pages={101570} }
+
+@article{idrid,
+title = {IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge},
+journal = {Medical Image Analysis},
+volume = {59},
+pages = {101561},
+year = {2020},
+issn = {1361-8415},
+doi = {https://doi.org/10.1016/j.media.2019.101561},
+url = {https://www.sciencedirect.com/science/article/pii/S1361841519301033},
+author = {Prasanna Porwal and Samiksha Pachade and Manesh Kokare and Girish Deshmukh and Jaemin Son and Woong Bae and Lihong Liu and Jianzong Wang and Xinhui Liu and Liangxin Gao and TianBo Wu and Jing Xiao and Fengyan Wang and Baocai Yin and Yunzhi Wang and Gopichandh Danala and Linsheng He and Yoon Ho Choi and Yeong Chan Lee and Sang-Hyuk Jung and Zhongyu Li and Xiaodan Sui and Junyan Wu and Xiaolong Li and Ting Zhou and Janos Toth and Agnes Baran and Avinash Kori and Sai Saketh Chennamsetty and Mohammed Safwan and Varghese Alex and Xingzheng Lyu and Li Cheng and Qinhao Chu and Pengcheng Li and Xin Ji and Sanyuan Zhang and Yaxin Shen and Ling Dai and Oindrila Saha and Rachana Sathish and Tânia Melo and Teresa Araújo and Balazs Harangi and Bin Sheng and Ruogu Fang and Debdoot Sheet and Andras Hajdu and Yuanjie Zheng and Ana Maria Mendonça and Shaoting Zhang and Aurélio Campilho and Bin Zheng and Dinggang Shen and Luca Giancardo and Gwenolé Quellec and Fabrice Mériaudeau},
+keywords = {Diabetic Retinopathy, Retinal image analysis, Deep learning, Challenge},
+abstract = {Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on “Diabetic Retinopathy – Segmentation and Grading” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.}
+}
+
+@article{deepdrid,
+title = {DeepDRiD: Diabetic Retinopathy—Grading and Image Quality Estimation Challenge},
+journal = {Patterns},
+volume = {3},
+number = {6},
+pages = {100512},
+year = {2022},
+issn = {2666-3899},
+doi = {https://doi.org/10.1016/j.patter.2022.100512},
+url = {https://www.sciencedirect.com/science/article/pii/S2666389922001040},
+author = {Ruhan Liu and Xiangning Wang and Qiang Wu and Ling Dai and Xi Fang and Tao Yan and Jaemin Son and Shiqi Tang and Jiang Li and Zijian Gao and Adrian Galdran and J.M. Poorneshwaran and Hao Liu and Jie Wang and Yerui Chen and Prasanna Porwal and Gavin Siew {Wei Tan} and Xiaokang Yang and Chao Dai and Haitao Song and Mingang Chen and Huating Li and Weiping Jia and Dinggang Shen and Bin Sheng and Ping Zhang},
+keywords = {diabetic retinopathy, screening, deep learning, artificial intelligence, challenge, retinal image, image quality analysis, ultra-widefield, fundus image},
+abstract = {Summary
+We described a challenge named “Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge” in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.}
+}
+
+@article{drimdb,
+author = {Ugur Sevik and Cemal Kose and Tolga Berber and Hidayet Erdol},
+title = {{Identification of suitable fundus images using automated quality assessment methods}},
+volume = {19},
+journal = {Journal of Biomedical Optics},
+number = {4},
+publisher = {SPIE},
+pages = {046006},
+keywords = {Image quality, Image segmentation, Image processing, Image analysis, Feature extraction, Feature selection, Blood vessels, Databases, Diagnostics, Visualization},
+year = {2014},
+doi = {10.1117/1.JBO.19.4.046006},
+URL = {https://doi.org/10.1117/1.JBO.19.4.046006}
+}
+