ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation
Authors: Xiuquan Du, Xuebin Xu, Kunpeng Ma
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed approach is evaluated on the Endo Scene, Kvasir SEG and CVC-Colon DB datasets with ten evaluation metrics, and gives competitive results compared with other state-of-the-art methods in both learning ability and generalization capability. |
| Researcher Affiliation | Academia | Xiuquan Du1,2, , Xuebin Xu2 and Kunpeng Ma2 1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, China 2School of Computer Science and Technology, Anhui University, Hefei, China |
| Pseudocode | No | The paper describes the proposed modules and their operations textually and visually through figures, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper includes a link to supplementary material ('https:/ICGNet.github.io/Material.pdf'), but it does not explicitly state that this material contains the source code for the methodology. It only refers to 'Supplementary material'. |
| Open Datasets | Yes | We evaluate our proposed methods on three benchmark polyp datasets: Endo Scene [V azquez et al., 2017], Kvasir SEG [Jha et al., 2020], CVC-Colon DB [Bernal et al., 2012]. |
| Dataset Splits | Yes | We refer to the setting in [Zhang et al., 2020] to divide the training set, validation set and test set. |
| Hardware Specification | No | The authors acknowledge the High-performance Computing Platform of Anhui University for providing computing resources. This statement is too general and does not provide specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions deep learning and convolutional networks but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | More details of hyperparameters and loss function are reported in the implementation details and loss function respectively of supplementary material. |