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..
Automated Segmentation of Overlapping Cytoplasm in Cervical Smear Images via Contour Fragments
Authors: Youyi Song, Jing Qin, Baiying Lei, Kup-Sze Choi
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using two cervical smear datasets, the performance of our method is extensively evaluated and compared with that of the stateof-the-art approaches; the results show the superiority of the proposed method. |
| Researcher Affiliation | Academia | Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, China |
| Pseudocode | No | The paper describes its algorithms and methods in detail using text and mathematical equations, but it does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | This dataset is obtained from the website of ISBI 2015 Overlapping Cervical Cytology Image Segmentation Challenge. |
| Dataset Splits | No | All these four parameters are optimized by the cross validation procedure on a small training dataset, and our experimental ๏ฌndings of these parameters are follows. |
| Hardware Specification | Yes | All experiments are conducted on a PC with a 2.20 GHz Intel Core i5 CPU and 4.00 GB of RAM, and they are all implemented in MATLAB. |
| Software Dependencies | No | The paper states that experiments are 'implemented in MATLAB' but does not specify the version of MATLAB or any other software dependencies with version numbers. |
| Experiment Setup | Yes | When using a small value of penalty rate ฮพ, the role of this energy term is diminished; however, when using a large value of it, it is more likely to lead to turbulence of graph s energy, so that graph s convergence cannot be reached. It is set as 1.5 in both datasets. The value of ฯ roughly depends on the overlapping degree in the dataset; the higher overlapping degree, the larger value. In our experiments, we set it to 7 in the Pap stain dataset, and to 10 in the H&E dataset. The values of ฯ1 and ฯ2 rely on imaging quality and overlapping degree. When images with high imaging quality and low overlapping degree, they both should be set a larger value; otherwise, curvature s role should be stressed greater. They are set as 1 and 0.2 in the Pap dataset, and as 0.7 and 0.4 in the H&E dataset, respectively. |