Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Polyper: Boundary Sensitive Polyp Segmentation
Authors: Hao Shao, Yang Zhang, Qibin Hou
AAAI 2024 | Venue PDF | 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. |