We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| adgaudio/O-MedAL | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| pick_initial_data_points_to_label | Ran | adgaudio/O-MedAL/medal/model_configs/medal.py code served (permissive licence) · get_code("822243a912290ae6") |
| get_labeled_and_topk_unlabeled_embeddings | Not yet run | adgaudio/O-MedAL/medal/model_configs/medal.py code served (permissive licence) · get_code("e673f6b60c577301") |
| pick_data_points_to_label | Not yet run | adgaudio/O-MedAL/medal/model_configs/medal.py code served (permissive licence) · get_code("63b443838f812f7d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multi-class tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.10508")
get_code_for_paper("1908.10508")
have("1908.10508")
Connect an agent — have() is free.