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 | Conference PDF | Archive PDF | Plain Text | 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. |