FLARE
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Medical Image Segmentation | FLARE 2021 | Dice Score96.85 | 89 | |
| Medical Image Segmentation | FLARE | Mean Dice95.21 | 45 | |
| LiDAR Semantic Segmentation | FLARE (test) | Flare IoU78.58 | 16 | |
| Abdominal Organ Segmentation | FLARE 22 (test) | Mean Dice70.4 | 16 | |
| Medical Image Segmentation and Calibration | FLARE | ECE2.5 | 16 | |
| Organ Segmentation | FLARE External | Mean DSC88.54 | 14 | |
| Organ Segmentation | FLARE Internal | Mean DSC95.02 | 14 | |
| Abdominal organ segmentation | FLARE 23 | Duration28 | 10 | |
| Pan-cancer Screening | FLARE 2023 | DSC56.7 | 10 | |
| Medical Image Segmentation | FLARE22 | Recall94.92 | 9 | |
| Nighttime Flare Removal | Flare7K++ Synthetic Images | PSNR29.798 | 9 | |
| Organ Segmentation | FLARE22 | DSC Liv.97.7 | 9 | |
| Memorization and Privacy Analysis | Flare-F | NN Distance (DNN)0.9723 | 8 | |
| Segmentation | FLARE22 | Dice Score92 | 8 | |
| 3D Medical Image Segmentation | FLARE 2021 (test) | DSC88.6 | 8 | |
| Financial Language Analysis | Flare CFA | Accuracy75.8 | 8 | |
| Tabular Data Synthesis | flare | Training Time (s)6.2 | 7 | |
| Segmentation | FLARE | DSC0.868 | 7 | |
| Medical Image Segmentation Explainability Evaluation | FLARE 2022 | GFLOPs94,431 | 6 | |
| Video segmentation | FLARE 2022 (test) | Liver97.65 | 5 | |
| Classification | Flare | Accuracy51.8 | 4 | |
| Classification | Flare (test) | Accuracy (FNS)84.1 | 4 | |
| FBP-UNet Reconstruction | FLARE23 (train) | Risk0.057 | 4 | |
| Denoising | FLARE 23 (train) | Risk0.056 | 4 | |
| Depth correction | FLARE Foreground regions | MSE0.0219 | 2 |
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