FLARE
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Medical Image Segmentation | FLARE | Mean Dice94.4 | 17 | |
| Medical Image Segmentation and Calibration | FLARE | ECE2.5 | 16 | |
| Abdominal organ segmentation | FLARE 23 | Duration28 | 10 | |
| Pan-cancer Screening | FLARE 2023 | DSC56.7 | 10 | |
| Nighttime Flare Removal | Flare7K++ Synthetic Images | PSNR29.798 | 9 | |
| Organ Segmentation | FLARE22 | DSC Liv.97.7 | 9 | |
| 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 | |
| Abdominal Organ Segmentation | FLARE 22 (test) | Mean Dice70.4 | 2 | |
| Persistent Homology Computation | FLARE22 | Latency (s)2.54 | 2 | |
| Patch Summarization | FLARE22 | Accuracy0.8754 | 2 | |
| Medical Image Segmentation | Flare 23 (test) | DSC90.07 | 2 |
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