Contents

What are the live D4 leaderboards?

The live D4 leaderboards allow participants to submit new models and evaluate them on the D4 test set, the same one used for the July 2019 results. We provide two leaderboards, one based on the D2 longitudinal prediction set and the other on the cross-sectional D3 prediction set. The leaderboards are updated every 5 minutes. 

We already populated the tables with the prize-eligible submissions that were evaluated in June. 

How to submit

Participants should name their submission file as:

TADPOLE_Submission_D4Live_D2_TeamName-Index.csv

This will enable our script to pick up the submission file automatically. One example name is TADPOLE_Submission_D4Live_D2_PowerRangers1.csv. No underscores should be included in team names. For predictions using the cross-sectional D3 prediction set, participants should tag their submissions as: 

TADPOLE_Submission_D4Live_D3_TeamName-Index.csv

The index number is to avoid name clashes. If a submission already exists with the same name, it will automatically get a small hash appended to its name.

If helpful, the script performing the live evaluation can be found on our github repository here. The format of the prediction is the same as before, see submission page for details. 

Live Leaderboard for longitudinal prediction set (D2)

Table last updated on 2020-08-19 17:40 (BST +0). Update frequency: ad hoc basis (contact: tadpole@cs.ucl.ac.uk)

RANK
FILE NAME MAUC RANK MAUC BCA ADAS RANK ADAS MAE ADAS WES ADAS CPA VENTS RANK VENTS MAE VENTS WES VENTS CPA DATE
1.0 NICVICOROB-mean 1.0 0.935 0.825 1.0 3.44 3.44 0.49 2.0 0.40 0.40 0.50 2020-08-19 16:33
2.0 Frog 2.0 0.931 0.849 6.0 4.85 4.74 0.44 12.0 0.45 0.33 0.47 2019-06-14 14:00
3.0 CBIL-MinMFa 4.0 0.909 0.845 17.0 5.53 5.54 0.39 15.0 0.46 0.46 0.02 2019-08-12 14:46
4.0 CBIL-MinMF1 16.0 0.886 0.818 8.0 5.10 5.11 0.41 16.0 0.46 0.46 0.37 2019-08-09 06:57
5.0 EMC1-Std 10.0 0.898 0.811 28.5 6.05 5.40 0.45 3.5 0.41 0.29 0.43 2019-06-14 14:00
6.0 VikingAI-Sigmoid 22.0 0.875 0.760 10.0 5.20 5.11 0.02 13.5 0.45 0.35 0.20 2019-06-14 14:00
7.0 EMC1-Custom 14.0 0.892 0.798 28.5 6.05 5.40 0.45 3.5 0.41 0.29 0.43 2019-06-14 14:00
8.0 CBIL 11.0 0.897 0.803 19.0 5.66 5.65 0.37 17.0 0.46 0.46 0.09 2019-06-14 14:00
9.0 Apocalypse 9.0 0.902 0.827 18.0 5.57 5.57 0.50 24.0 0.52 0.52 0.50 2019-06-14 14:00
10.0 GlassFrog-Average 7.0 0.902 0.825 11.0 5.26 5.27 0.26 34.0 0.68 0.60 0.33 2019-06-14 14:00
11.0 GlassFrog-SM 7.0 0.902 0.825 21.0 5.77 5.92 0.20 25.0 0.52 0.33 0.20 2019-06-14 14:00
12.0 BORREGOTECMTY 25.0 0.866 0.808 24.0 5.90 5.82 0.39 7.0 0.43 0.37 0.40 2019-06-14 14:00
13.0 BenchmarkMixedEffects 32.0 0.846 0.706 2.0 4.19 4.19 0.31 27.0 0.56 0.56 0.50 2019-06-14 14:00
14.0 EMC-EB 5.0 0.907 0.805 46.0 6.75 6.66 0.50 11.0 0.45 0.40 0.48 2019-06-14 14:00
15.0 VikingAI-Logistic 26.0 0.865 0.754 26.0 6.02 5.91 0.26 13.5 0.45 0.35 0.20 2019-06-14 14:00
16.0 lmaUCL-Covariates 28.0 0.852 0.760 33.0 6.28 6.29 0.28 5.0 0.42 0.41 0.11 2019-06-14 14:00
17.0 CN2L-Average 35.0 0.843 0.792 12.0 5.31 5.31 0.35 20.0 0.49 0.49 0.33 2019-06-14 14:00
18.5 CN2L-RandomForest 12.0 0.896 0.792 20.0 5.73 5.73 0.42 36.0 0.71 0.71 0.41 2019-06-14 14:00
18.5 lmaUCL-Std 27.0 0.859 0.781 35.0 6.30 6.33 0.26 6.0 0.42 0.41 0.09 2019-06-14 14:00
20.5 FortuneTellerFish-SuStaIn 48.0 0.806 0.685 5.0 4.81 4.81 0.21 18.0 0.49 0.49 0.18 2019-06-14 14:00
20.5 CN2L-NeuralNetwork 49.0 0.783 0.717 13.0 5.36 5.36 0.34 9.0 0.44 0.44 0.27 2019-06-14 14:00
22.0 BenchmarkMixedEffectsAPOE 43.0 0.822 0.749 4.0 4.75 4.75 0.36 28.0 0.57 0.57 0.40 2019-06-14 14:00
23.0 Tohka-Ciszek-RandomForestLin 23.0 0.875 0.796 27.0 6.03 6.03 0.15 26.0 0.56 0.56 0.37 2019-06-14 14:00
24.0 BGU-LSTM 17.0 0.883 0.779 30.0 6.09 6.12 0.39 30.0 0.60 0.60 0.23 2019-06-14 14:00
25.0 DIKU-GeneralisedLog-Custom 19.0 0.878 0.790 14.5 5.40 5.40 0.26 46.5 1.05 1.05 0.05 2019-06-14 14:00
26.5 DIKU-GeneralisedLog-Std 20.0 0.877 0.790 14.5 5.40 5.40 0.26 46.5 1.05 1.05 0.05 2019-06-14 14:00
26.5 AlgosForGood 30.0 0.847 0.810 16.0 5.46 5.11 0.13 35.0 0.69 3.31 0.19 2019-06-14 14:00
28.0 CyberBrains 42.0 0.823 0.747 9.0 5.16 5.16 0.24 31.0 0.62 0.62 0.12 2019-06-14 14:00
29.0 lmaUCL-halfD1 34.0 0.845 0.753 45.0 6.53 6.51 0.31 8.0 0.44 0.42 0.13 2019-06-14 14:00
30.0 ATRI-Biostat-MA-2 13.0 0.893 0.773 34.0 6.29 5.92 0.22 41.0 1.00 0.91 0.24 2019-07-30 21:45
31.0 Mayo-BAI-ASU 59.0 0.691 0.624 7.0 4.98 4.98 0.32 23.0 0.52 0.52 0.40 2019-06-14 14:00
32.0 BGU-RF 36.0 0.838 0.673 36.5 6.33 6.10 0.35 21.5 0.50 0.38 0.26 2019-06-14 14:00
33.0 ATRI-Biostat-LTJMM-2 18.0 0.883 0.780 25.0 6.00 5.68 0.24 52.0 1.11 1.09 0.34 2019-07-30 21:45
34.5 ATRI-Biostat-JMM-2 15.0 0.892 0.770 32.0 6.27 6.02 0.29 51.0 1.10 1.06 0.35 2019-07-30 21:45
34.5 BGU-RFFIX 40.0 0.831 0.673 36.5 6.33 6.10 0.35 21.5 0.50 0.38 0.26 2019-06-14 14:00
36.0 FortuneTellerFish-Control 39.0 0.834 0.692 3.0 4.70 4.70 0.22 60.0 1.38 1.38 0.50 2019-06-14 14:00
37.0 GlassFrog-LCMEM-HDR 7.0 0.902 0.825 38.0 6.34 6.21 0.47 61.0 1.66 1.59 0.41 2019-06-14 14:00
38.0 SBIA 50.0 0.776 0.721 50.0 7.10 7.38 0.40 10.0 0.44 0.31 0.13 2019-06-14 14:00
39.0 Chen-MCW-Stratify 29.0 0.848 0.783 43.5 6.48 6.24 0.23 42.5 1.01 1.00 0.11 2019-06-14 14:00
40.0 Rocket 62.0 0.680 0.519 22.0 5.81 5.71 0.34 33.0 0.64 0.64 0.29 2019-06-14 14:00
41.0 BenchmarkSVM 38.0 0.836 0.764 47.0 6.82 6.82 0.42 37.0 0.86 0.84 0.50 2019-06-14 14:00
42.0 Chen-MCW-Std 37.0 0.836 0.778 43.5 6.48 6.24 0.23 42.5 1.01 1.00 0.11 2019-06-14 14:00
43.0 DIKU-ModifiedMri-Custom 44.5 0.807 0.670 40.5 6.44 6.44 0.27 39.5 0.92 0.92 0.01 2019-06-14 14:00
44.0 DIVE 57.0 0.708 0.568 49.0 7.10 7.10 0.34 19.0 0.49 0.49 0.13 2019-06-14 14:00
45.0 DIKU-ModifiedMri-Std 46.5 0.806 0.670 40.5 6.44 6.44 0.27 39.5 0.92 0.92 0.01 2019-06-14 14:00
46.0 ITESMCEM 61.0 0.680 0.657 31.0 6.26 6.26 0.35 38.0 0.92 0.92 0.43 2019-06-14 14:00
47.0 BenchmarkLastVisit 51.5 0.774 0.792 48.0 7.05 7.05 0.45 32.0 0.63 0.61 0.47 2019-06-14 14:00
48.0 Sunshine-Conservative 33.0 0.845 0.816 51.5 7.90 7.90 0.50 53.5 1.12 1.12 0.50 2019-06-14 14:00
49.0 BravoLab 53.0 0.771 0.682 60.0 8.22 8.22 0.49 29.0 0.58 0.58 0.41 2019-06-14 14:00
50.0 DIKU-ModifiedLog-Custom 44.5 0.807 0.670 40.5 6.44 6.44 0.27 57.5 1.17 1.17 0.06 2019-06-14 14:00
51.0 DIKU-ModifiedLog-Std 46.5 0.806 0.670 40.5 6.44 6.44 0.27 57.5 1.17 1.17 0.06 2019-06-14 14:00
52.0 Sunshine-Std 41.0 0.825 0.771 51.5 7.90 7.90 0.50 53.5 1.12 1.12 0.50 2019-06-14 14:00
53.0 chizhu-1 31.0 0.847 0.496 65.0 19.01 19.01 0.50 66.0 94.31 94.31 0.41 2019-08-22 06:28
54.5 chizhu-3 64.5 0.641 0.653 54.5 8.01 8.01 0.43 44.5 1.02 1.02 0.50 2019-08-22 07:05
54.5 chizhu-4 64.5 0.641 0.653 54.5 8.01 8.01 0.43 44.5 1.02 1.02 0.50 2019-08-22 07:14
56.0 Billabong-UniAV45 55.0 0.720 0.616 61.5 9.22 8.82 0.29 49.5 1.09 0.99 0.45 2019-06-14 14:00
57.0 Billabong-Uni 56.0 0.718 0.622 61.5 9.22 8.82 0.29 49.5 1.09 0.99 0.45 2019-06-14 14:00
58.0 chizhu-5 60.0 0.687 0.653 57.0 8.03 8.03 0.43 62.0 2.95 2.95 0.40 2019-08-22 09:43
59.5 chizhu-5_dvXMdTe 58.0 0.694 0.653 58.0 8.06 8.06 0.43 65.0 2.97 2.97 0.40 2019-09-28 03:23
59.5 Billabong-Multi 67.0 0.541 0.556 66.0 27.01 19.90 0.46 48.0 1.07 1.07 0.45 2019-06-14 14:00
61.5 chizhu-1_Dr22rrB 64.5 0.641 0.653 54.5 8.01 8.01 0.43 63.5 2.97 2.97 0.40 2019-08-22 06:53
61.5 chizhu-2 64.5 0.641 0.653 54.5 8.01 8.01 0.43 63.5 2.97 2.97 0.40 2019-08-22 06:58
63.0 Billabong-MultiAV45 68.0 0.527 0.530 67.0 28.45 21.22 0.47 55.0 1.13 1.07 0.47 2019-06-14 14:00
64.0 BIGS2 69.0 0.455 0.488 63.0 11.62 14.65 0.50 59.0 1.20 1.12 0.07 2019-06-14 14:00
999 Threedays 3.0 0.921 0.823 - - - - - - - - 2019-06-14 14:00
999 ARAMIS-Pascal 21.0 0.876 0.850 - - - - - - - - 2019-06-14 14:00
999 IBM-OZ-Res 24.0 0.868 0.766 - - - - 56.0 1.15 1.15 0.50 2019-06-14 14:00
999 Orange 51.5 0.774 0.792 - - - - - - - - 2019-06-14 14:00
999 SMALLHEADS-NeuralNet 54.0 0.737 0.605 64.0 13.87 13.87 0.41 - - - - 2019-06-14 14:00
999 SMALLHEADS-LinMixedEffects - - - 59.0 8.09 7.94 0.04 - - - - 2019-06-14 14:00
999 Tohka-Ciszek-SMNSR - - - 23.0 5.87 5.87 0.14 - - - - 2019-06-14 14:00
999 uncchit-v2 - - - - - - - 1.0 0.39 0.33 0.35 2020-06-02 11:24

Live Leaderboard for cross-sectional prediction set (D3)

Table last updated on 2020-01-16 11:45 (BST +0). Update frequency: ad hoc basis (contact: tadpole@cs.ucl.ac.uk)

RANK
FILE NAME MAUC RANK MAUC BCA ADAS RANK ADAS MAE ADAS WES ADAS CPA VENTS RANK VENTS MAE VENTS WES VENTS CPA DATE
1.0 GlassFrog-Average 3.0 0.897 0.826 7.0 5.86 5.57 0.25 3.0 0.68 0.55 0.24 2019-06-14 14:00
2.0 GlassFrog-LCMEM-HDR 3.0 0.897 0.826 12.0 6.57 6.56 0.34 1.0 0.48 0.38 0.24 2019-06-14 14:00
3.0 GlassFrog-SM 3.0 0.897 0.826 6.0 5.77 5.77 0.19 9.0 0.82 0.55 0.07 2019-06-14 14:00
4.0 Tohka-Ciszek-RandomForestLin 11.0 0.865 0.786 4.0 4.92 4.92 0.10 10.0 0.83 0.83 0.35 2019-06-14 14:00
5.0 ATRI-Biostat-LTJMM-2 12.0 0.862 0.811 3.0 4.86 4.26 0.00 13.0 0.93 0.95 0.22 2019-07-30 21:45
6.5 VikingAI-Logistic 8.0 0.876 0.768 8.0 5.94 5.91 0.22 23.0 1.04 1.01 0.18 2019-06-14 14:00
6.5 Rocket 10.0 0.865 0.771 5.0 5.27 5.14 0.39 24.0 1.06 1.06 0.27 2019-06-14 14:00
8.0 ATRI-Biostat-MA-2 19.0 0.841 0.801 9.0 6.12 5.42 0.12 12.0 0.92 0.94 0.22 2019-07-30 21:45
10.0 lmaUCL-Std 14.0 0.854 0.698 21.0 6.95 6.93 0.05 6.0 0.81 0.81 0.22 2019-06-14 14:00
10.0 lmaUCL-Covariates 14.0 0.854 0.698 21.0 6.95 6.93 0.05 6.0 0.81 0.81 0.22 2019-06-14 14:00
10.0 lmaUCL-halfD1 14.0 0.854 0.698 21.0 6.95 6.93 0.05 6.0 0.81 0.81 0.22 2019-06-14 14:00
12.0 ATRI-Biostat-JMM-2 18.0 0.842 0.801 14.0 6.67 6.15 0.31 11.0 0.90 0.91 0.25 2019-07-30 21:45
13.0 EMC1-Std 31.0 0.705 0.567 10.0 6.29 6.19 0.47 4.0 0.80 0.62 0.48 2019-06-14 14:00
14.0 SBIA 30.0 0.779 0.782 13.0 6.63 6.43 0.40 8.0 0.82 0.75 0.18 2019-06-14 14:00
16.5 BGU-LSTM 6.0 0.877 0.776 18.0 6.75 6.17 0.39 28.0 1.11 0.79 0.17 2019-06-14 14:00
16.5 BGU-RFFIX 6.0 0.877 0.776 18.0 6.75 6.17 0.39 28.0 1.11 0.79 0.17 2019-06-14 14:00
16.5 BGU-RF 6.0 0.877 0.776 18.0 6.75 6.17 0.39 28.0 1.11 0.79 0.17 2019-06-14 14:00
16.5 BenchmarkMixedEffects 20.0 0.839 0.728 1.0 4.23 4.23 0.34 31.0 1.13 1.13 0.50 2019-06-14 14:00
19.0 BravoLab 22.0 0.813 0.730 31.0 8.02 8.02 0.47 2.0 0.64 0.64 0.42 2019-06-14 14:00
20.5 BORREGOTECMTY 16.0 0.852 0.748 11.0 6.44 5.86 0.46 32.0 1.14 1.02 0.49 2019-06-14 14:00
20.5 CyberBrains 21.0 0.830 0.755 2.0 4.72 4.72 0.21 36.0 1.54 1.54 0.50 2019-06-14 14:00
22.0 EMC-EB 9.0 0.869 0.765 30.0 7.71 7.91 0.50 22.0 1.03 1.07 0.49 2019-06-14 14:00
23.0 DIKU-GeneralisedLog-Std 23.0 0.798 0.684 24.5 6.99 6.99 0.17 18.5 0.95 0.95 0.05 2019-06-14 14:00
24.0 DIKU-GeneralisedLog-Custom 24.0 0.798 0.681 24.5 6.99 6.99 0.17 18.5 0.95 0.95 0.05 2019-06-14 14:00
25.0 Billabong-Uni 32.0 0.704 0.626 15.5 6.69 6.69 0.38 20.5 0.98 0.98 0.48 2019-06-14 14:00
26.5 DIKU-ModifiedLog-Std 25.5 0.798 0.688 27.5 7.10 7.10 0.17 15.5 0.95 0.95 0.05 2019-06-14 14:00
26.5 DIKU-ModifiedMri-Std 25.5 0.798 0.688 27.5 7.10 7.10 0.17 15.5 0.95 0.95 0.05 2019-06-14 14:00
28.0 Billabong-UniAV45 33.0 0.703 0.620 15.5 6.69 6.69 0.38 20.5 0.98 0.98 0.48 2019-06-14 14:00
29.5 DIKU-ModifiedLog-Custom 27.5 0.798 0.691 27.5 7.10 7.10 0.17 15.5 0.95 0.95 0.05 2019-06-14 14:00
29.5 DIKU-ModifiedMri-Custom 27.5 0.798 0.691 27.5 7.10 7.10 0.17 15.5 0.95 0.95 0.05 2019-06-14 14:00
31.0 CBIL 17.0 0.847 0.780 35.0 10.99 11.65 0.49 30.0 1.12 1.12 0.39 2019-06-14 14:00
32.0 BenchmarkLastVisit 29.0 0.785 0.771 23.0 6.97 7.07 0.42 34.0 1.17 0.64 0.11 2019-06-14 14:00
33.0 Billabong-MultiAV45 34.0 0.682 0.603 32.5 9.30 9.30 0.43 25.5 1.09 1.09 0.49 2019-06-14 14:00
34.0 Billabong-Multi 35.0 0.681 0.605 32.5 9.30 9.30 0.43 25.5 1.09 1.09 0.49 2019-06-14 14:00
35.0 BenchmarkSVM 37.0 0.494 0.490 34.0 10.01 10.01 0.42 33.0 1.15 1.18 0.50 2019-06-14 14:00
36.0 DIVE 36.0 0.512 0.498 36.0 16.66 16.74 0.41 35.0 1.42 1.42 0.34 2019-06-14 14:00
999 IBM-OZ-Res 1.0 0.905 0.830 - - - - 37.0 1.77 1.77 0.50 2019-06-14 14:00

Legend:

  • MAUC - Multiclass Area Under the Curve
  • BCA - Balanced Classification Accuracy
  • MAE - Mean Absolute Error
  • WES - Weighted Error Score
  • CPA - Coverage Probability Accuracy for 50% Confidence Interval
  • ADAS - Alzheimer's Disease Assessment Scale Cognitive (13)
  • VENTS - Ventricle Volume
  • RANK - We first compute the sum of ranks from MAUC, ADAS MAE and VENTS MAE, then derive the final ranking from these sums of ranks. For example, the top leaderboard entry will have the lowest sum of ranks from these three categories.

Methods description for new leaderboard entries

For the new leaderboard entries you are happy with, please write a short description of the algorithm you used here. This way, the community can have an idea of the approach you used, and are able to cite any papers/code that you link there.

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