We lifted 6 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| hhase/spinal-navigation-rl | canonical | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| get_wrapper_class | Ran | hhase/spinal-navigation-rl/utils/utils.py code served (permissive licence) · get_code("aae5a05e55eb2d50") |
| plot2fig | Ran | hhase/spinal-navigation-rl/utils/visualization.py code served (permissive licence) · get_code("6ff3899d94eb4337") |
| reachability_plot | Ran | hhase/spinal-navigation-rl/utils/visualization.py code served (permissive licence) · get_code("8971a11fb41bc5ef") |
| flatten_dict_observations | Not yet run | hhase/spinal-navigation-rl/utils/utils.py code served (permissive licence) · get_code("c121177b1f250612") |
| timeit | Not yet run | hhase/spinal-navigation-rl/utils/resnet.py code served (permissive licence) · get_code("73499fef39e18575") |
| variable_with_weight_decay | Not yet run | hhase/spinal-navigation-rl/utils/resnet.py code served (permissive licence) · get_code("4144931509de52a8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when to terminate the task. Our method is trained and evaluated on an in-house collected data-set of 34 volunteers and when compared to pure RL and supervised learning (SL) techniques, it performs substantially better, which highlights the suitability of RL navigation for US-guided procedures. When testing our proposed model, we obtained a 82.91% chance of navigating correctly to the sacrum from 165 different starting positions on 5 different unseen simulated environments.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.13321")
get_code_for_paper("2003.13321")
have("2003.13321")
Connect an agent — have() is free.