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First Results and Analysis:

Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, et al. "IDRiD: Diabetic Retinopathy–Segmentation and Grading Challenge." Medical image analysis 59 (2020): 101561. DOI:  style="">https://doi.org/10.1016/j.media.2019.101561

Onsite

Challenge Results:
                                                 Subchallenge - 2
Rank DR and DME Grading
  Team Name DR (Ac) DME (Ac) DR+DME (Ac)
1 LzyUNCC 0.7476
 0.8058
 0.6311
2 VRT 0.5922
 0.8155
 0.5534
3 Mammoth 0.5437
 0.8350
 0.5146
4 HarangiM1 0.5534
 0.7476
 0.4757
4 AVSASVA 0.5534
 0.8058
 0.4757
5 HarangiM2 0.4757
 0.7282
 0.4078
Ac - Accuracy

Note regarding DR+DME (Ac): Challenge is to detect DR and DME both simultaneously. e.g. For An Image -> DR (Grade = 3) and DME(Grade = 2)

If participating solution satisfies both conditions then it is counted as one, else zero. Similarly, for all images, it is computed to get the total no of true (one) instances. The final count (total no of true instances) is divided by a total number of images to get the average accuracy.
                                                    Subchallenge - 3
Rank Optic Disc Detection
  Team Name ED
1 DeepDR 21.072
2 VRT 33.538
3 ZJU-Bll-SGEX 33.875
4 SDNU 36.22
Rank Fovea Detection
  Team Name ED
1 DeepDR 64.492
2 VRT 68.466
3 SDNU 85.40
4 ZJU-Bll-SGEX 570.133
Rank Optic Disc Segmentation
  Team Name JD
1 ZJU-Bll-SGEX 0.9338
2 VRT 0.9305
3 IITkgpKLIV 0.8572
4 SDNU 0.7892

ED - Euclidean Distance JD - Jaccard index

Online Challenge Results:
Subchallenge - 1
Sr. No. Contact Person Team Member(s) Affiliation(s) Team Name MA Score RANK HE Score RANK SE Score RANK EX Score RANK
1 Jaemin Son VUNO Inc., Seoul, Republic of Korea VRT 0.4951 2 0.6804 1 0.6995 1 0.7127 11
2 Liu Lihong Ping An Technology (Shenzhen) CO.,Ltd, China PATech 0.474 3 0.649 2 -   0.885 1
3 Fengyan Wang iFLYTEK Research, China. iFLYTEK-MIG 0.5017 1 0.5588 3 0.6588 3 0.8741 2
4 Yunzhi Wang University of Oklahoma, US  SOONER 0.4003 5 0.5395 4 0.5369 7 0.739 10
5 Yoon Ho Choi Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea. SAIHST -   -   -   0.8582 3
6 Zhongyu Li University of North Carolina at Charlotte lzyuncc_fusion -   -   0.6259 4 0.8202 4
7 Xiaodan Sui Shandong Normal University, China
Southern Medical University, China
The University of North Carolina at Chapel Hill, USA		 SDNU 0.4111 4 0.4572 7 0.5374 6 0.5018 17
8 Hyunjun Eun KAIST, Republic of Korea.  CIL 0.392 6 0.4886 5 0.5024 8 0.7554 8
9 Manu Agarwal  Indian Institute of Technology Roorkee, India MedLabs 0.3397 8 0.3705 8 0.2637 10 0.7863 5
10 Zhengyi Li Peking University, China  AIMIA 0.3792 7 0.3283 10 0.2733 9 0.7662 6
11 Zhongyu Li University of North Carolina at Charlotte lzyuncc -   -   0.6607 2 0.7615 7
12 Deepak Anand Indian Institute of Technology Bombay, India MEDAL_IITB 0.2704 12 0.3705 9 -   0.7471 9
13 Qiong Bai   Edoctor -   -   -   0.7106 12
14 Tengfei Li The University of Texas MD Anderson Cancer Center, USA         Rice University, Houston, TX, USA                                              Southern Medical University, Guangzhou, China                            The University of North Carolina, Chapel Hill, NC, USA BIGS2 0.2912 11 0.1408 12 0.0479 13 0.6759 13
15 Chih-Hsuan Liu National Tsing Hua University, Taiwan NTHU-CVLab 0.2958 10 0.2915 11 0.2554 11 0.5965 14
16 Oindrila Saha Indian Institute of Technology Kharagpur, India  IITKgpKLIV 0.0059 14 0.0829 13 0.1823 12 0.5498 15
17 Xingzheng Lyu Zhejiang University, China
National University of Singapore, Singapore
Bioinformatics Institute, A*STAR, Singapore
Beijing Shanggong Medical Technology Co., Ltd., China 		 ZJU-BII-SGEX 0.2601 13 0.477 6 0.5467 5 0.5094 16
18 Xuechen Li Shenzhen University, China  SZU -   -   -   0.3718 18
19 Ling Dai Shanghai Jiao Tong Univ. , China  deepdr -   -   -   0.2052 19
20 Mhd Hasan Sarhan Technical University of Munich, Germany FAT1 0.3356 9 -   -   -  
21 Junyan Wu Cleerly, US
Virginia Tech, US  University at Buffalo, US		   *   *   *   *  
22 Sagar B Hathwar PES University, India  Error 404 -   -   -   !  

*Data inconsistent with the format mentioned on the website

! Test results not submitted.

Note: Ranking of the teams for Subchallenge-1 is done based on the single score computed by finding the area under the Positive Predictive Value (Precision) and Sensitivity (Recall) curve using the test data of apparent retinopathy.
Subchallenge - 2
Sr. No. Contact Person Team Member(s) Affiliation(s) Team Name Accuracy
1 Junyan Wu Cleerly, US
Virginia Tech, US  University at Buffalo, US		 Mammoth 0.9322
2 Xiaodan Sui Shandong Normal University, China
Southern Medical University, China
The University of North Carolina at Chapel Hill, USA 		 SDNU 0.8789
3 Balazs Harangi University of Debrecen, Hungary HarangiM1 0.8741
4 Balazs Harangi University of Debrecen, Hungary HarangiM2 0.8692
5 Varghese Alex Individual Researcher, India  AVSASVA 0.8426
6 Jaemin Son VUNO Inc., Seoul, Republic of Korea VRT 0.7554
7 Zhongyu Li University of North Carolina at Charlotte lzyuncc 0.5327
8 Siddhesh Thakur Shri Guru Gobind Singhji Institute of  Engineering & Technology, India Py 0.5206
9 Xuechen Li Shenzhen University, China  SZU 0.4964
10 K V Sai Sundar Sri Sathya Sai Institute of Higher Learning, India SS 0.4843
11 Xingzheng Lyu Zhejiang University, China
Bioinformatics Institute, A*STAR, Singapore                                                National University of Singapore, Singapore
Beijing Shanggong Medical Technology Co., Ltd., China		 ZJU-BII-SGEX 0.3995
12 Ling Dai Shanghai Jiao Tong Univ. , China  deepdr 0.3148
13 Chih-Hsuan Liu National Tsing Hua University, Taiwan  NTHU-CVLab 0.1719
Subchallenge - 3
Sr. No. Contact Person  Team Member(s) Affiliation(s) Team Name Optic Disc Detection  (ED) Fovea Detection (ED) Optic Disc   Segmentation (J)
1 Xingzheng Lyu Zhejiang University, China
National University of Singapore, Singapore
Bioinformatics Institute, A*STAR, Singapore
Beijing Shanggong Medical Technology Co., Ltd., China 		 ZJU-BII-SGEX 25.6157 (1) 45.8959 0.9826 (1)
2 Jaemin Son VUNO Inc., Seoul, Republic of Korea VRT 26.6377 (2) 39.2085 (3) 0.0853
3 Manu Agarwal Indian Institute of Technology Roorkee, India MedLabs 48.433 33.253 (1) 0.4904
4 Xiaodan Sui Shandong Normal University, China
Southern Medical University, China
The University of North Carolina at Chapel Hill, USA 		 SDNU 26.9181 (3) 72.6426 0.8656
5 Ling Dai Shanghai Jiao Tong Univ. , China  deepdr 29.0037 39.1054 (2) -
6 Oindrila Saha Indian Institute of Technology Kharagpur, India IITKgpKLIV 65.9374 - 0.9640 (2)
7 Chaitanya Kaul University of York, United Kingdom AI@UOY - - 0.9605 (3)
8 Tânia Melo FEUP & INESC TEC, Porto, Portugal  CBER 30.8007 53.5337 0.8910
9 Alakh Desai CVIT, IIIT Hyderabad, India  Alchemist 30.3629 63.1369 0.8021
10 Zhengyi Li Peking University, China  AIMIA 43.8468 67.43 0.9522
11 Xuechen Li Shenzhen University, China  SZU 32.62 52.0077 *
12 Yunzhi Wang University of Oklahoma, US SOONER 37.92 97.0545 0.8170
13 Deepak Anand Indian Institute of Technology Bombay, India Medal IITB 48.433 - 0.9128
14 S M Jaisakthi VIT University, Vellore, India                                        SSN College of Engineering, Chennai, India SSNMLRG - RUN 2 54.2101 - 0.6651
15 Qunwei Hu University of Science and Technology of China, China One 59.7293 69.0946 0.8408
16 Sagar B Hathwar PES University, India  Error 404 61.847 114.5051 0.6916
17 Santhosh Kumar Sukumar  HTIC, Indian Institute of Technology Madras, India  HTIC_IIT_Madras (DL_Method) 61.9589 - *
18 S M Jaisakthi VIT University, Vellore, India                                        SSN College of Engineering, Chennai, India SSNMLRG - RUN 1 115.2691 - 0.6803
19 Kartik Teotia MIT Manipal, India MIT Manipal 154.4787 -  
20 Sidhartha Dey Manipal Institute of Technology                                    Rice University, USA Affine Snakes - - 0.8286
21 Santhosh Kumar Sukumar HTIC, Indian Institute of Technology Madras, India HTIC_IIT_Madras (Classical_Method) 161.9568   0.6122
22 Fatemeh Zabihollahy  Carleton University, Canada Carleton University - Medical Image Analysis - - 0.4594

*Data inconsistent with the format mentioned on the website

ED - Euclidean Distance

Note: Ranking of the teams for Optic Disc Segmentation is done based only on Jaccard Index (J) as the corresponding dice indices or sensitivity are not differing the rankings.