Polyper: Boundary Sensitive Polyp Segmentation
Authors: Hao Shao, Yang Zhang, Qibin Hou
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of Polyper, we conduct experiments on five publicly available challenging datasets, and receive state-of-the-art performance on all of them. |
| Researcher Affiliation | Academia | Hao Shao1, Yang Zhang2, Qibin Hou1* 1VCIP, School of Computer Science, Nankai University 2Department of Genetics and Cell Biology, College of Life Sciences, Nankai University |
| Pseudocode | No | The paper provides architectural diagrams and descriptions but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/haoshao-nku/medical seg.git. |
| Open Datasets | Yes | We report results on datasets used in Pranet (Fan et al. 2020), including: Kvasir-SEG, CVC-Clinc DB, CVC-Colon DB, Endo Scene, and ETIS. |
| Dataset Splits | No | The paper explicitly states the training and test sets but does not provide details on a separate validation set split. |
| Hardware Specification | Yes | All the experiments are conducted on one NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using PyTorch and mmsegmentation but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The input resolution during training is set to 224 224, and the batch size is set to 6. The number of iterations during training is 80k. We employ the Adam W optimizer for training with an initial learning rate of 0.0002, a momentum of 0.9, and a weight decay of 1e-4. |