We lifted 1 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 |
|---|---|---|
| mobarakol/3D_Attention_UNet | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| create_feature_maps | Ran | mobarakol/3D_Attention_UNet/segmentation/3d_attention_unet.py code served (permissive licence) · get_code("ab00532d77c54cbb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments to show the effect of each feature for overall survival (OS) prediction. The experimental results infer that radiomic features such as histogram, location, and shape of the necrosis region and clinical features like age are the most critical parameters to estimate the OS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.00985")
get_code_for_paper("2104.00985")
have("2104.00985")
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