Colonoscopy Coverage Deficiency via Depth algorithm, or C2D2, is a machine learning-based approach for improving colonoscopy coverage. The C2D2 network is a cascading structure. The inputs are 300 serialized gray images and the output is coverage. The C2D2_Lite_0_pt model is responsible for extracting the features of each image and the C2D2_Lite_1_pt model predicts a coverage value by inputting the characteristics of 300 pictures.
The following table lists the C2D2 Coverage Prediction models supported by the Vitis AI Library.
| No | Model Name | Framework |
|---|---|---|
| 1 | C2D2_Lite_0_pt | PyTorch |
| 2 | C2D2_Lite_1_pt | PyTorch |