Medical Image Classification on BraTS (test)
77.22AccuracyEG-AL
Evaluation results
| Method | Links | ||
|---|---|---|---|
| EG-ALSampling Strategy=Explainability-Guided, Total Labeled Samples=570, Active Learning Rounds=7, Backbone=DenseNet-121, Pre-training=ImageNet, Evaluation Protocol=5-shot episodes, Number of Seeds=52026.02 | 77.22 | 90 | |
| RandomSampling Strategy=Random, Total Labeled Samples=570, Active Learning Rounds=7, Backbone=DenseNet-121, Pre-training=ImageNet, Evaluation Protocol=5-shot episodes, Number of Seeds=52026.02 | 58.01 | 78.32 | |
| BaselineSampling Strategy=Random (initialization), Total Labeled Samples=150, Active Learning Rounds=0, Backbone=DenseNet-121, Pre-training=ImageNet, Evaluation Protocol=5-shot episodes, Number of Seeds=52026.02 | 45.1 | 67.52 |