Siqi Li

Orcid: 0000-0002-4569-1111

Affiliations:
  • Northeastern University, Department of Software College, Shenyang, China


According to our database1, Siqi Li authored at least 13 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
MTR-PET: Multi-temporal resolution PET images for lymphoma segmentation.
Biomed. Signal Process. Control., January, 2024

2022
Parallel 'same' and 'valid' convolutional block and input-collaboration strategy for histopathological image classification.
Appl. Soft Comput., 2022

2021
AW-SDRLSE: Adaptive Weighting and Scalable Distance Regularized Level Set Evolution for Lymphoma Segmentation on PET Images.
IEEE J. Biomed. Health Informatics, 2021

2020
DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images.
IEEE Access, 2020

2019
Stacked sparse autoencoder and case-based postprocessing method for nucleus detection.
Neurocomputing, 2019

Visibility Attribute Extraction and Anomaly Detection for Chinese Diagnostic Report Based on Cascade Networks.
IEEE Access, 2019

Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network.
IEEE Access, 2019

2018
Organ Location Determination and Contour Sparse Representation for Multiorgan Segmentation.
IEEE J. Biomed. Health Informatics, 2018

Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation.
Neurocomputing, 2018

An effective computer aided diagnosis model for pancreas cancer on PET/CT images.
Comput. Methods Programs Biomed., 2018

An Effective Multi-classification Method for NHL Pathological Images.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2018

2017
A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.
Comput. Math. Methods Medicine, 2017

Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.
Comput. Biol. Medicine, 2017


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