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..
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
Authors: Zunlei Feng, Zhonghua Wang, Xinchao Wang, Xiuming Zhang, Lechao Cheng, Jie Lei, Yuexuan Wang, Mingli Song1325-1333
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments demonstrate that, with only limited labeled patches, EVS-Net achieves a close performance of fully supervised methods, which provides a convenient tool for the pathological liver vessel segmentation. |
| Researcher Affiliation | Collaboration | Zunlei Feng1,5#, Zhonghua Wang1#, Xinchao Wang2, Xiuming Zhang1, Lechao Cheng3, Jie Lei4, Yuexuan Wang1 , Mingli Song1,5 1Zhejiang University 2Stevens Institute of Technology 3Zhejiang Lab 4Zhejiang University of Technology 5Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies |
| Pseudocode | Yes | Algorithm 1 The Training Algorithm for EVS-Net |
| Open Source Code | Yes | Code is publicly available at https://github.com/zju-vipa/EVS-Net. |
| Open Datasets | Yes | In this paper, we first collect a pathological liver image dataset containing 522 whole slide images with labels of vessels, MVI, and hepatocellular carcinoma grades. It can readily serve as a benchmark for the research on the analysis of pathological liver images. |
| Dataset Splits | Yes | The train, validation, and test sample number are 144, 000, 18, 000, and 18, 000, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'Deeplab V3+' as the segmentation network but does not provide specific version numbers for software dependencies or programming languages used. |
| Experiment Setup | Yes | In the experiment, the parameters are set as follows: ζ = 1, τ = 1, η = 2, λ = 10, ncritic = 5, the batch size T = 64, Adam hyperparameters for two discriminators α = 0.0001, β1 = 0, β2 = 0.9. The radius r1 and r2 are random values between 5 and 30. The radius r3 equals to 15. The learning rate for the segmentation network and two discriminators are all set to be 1e 4. |