Shenghua Cheng

Orcid: 0000-0003-3527-3845

According to our database1, Shenghua Cheng authored at least 14 papers between 2016 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.
IEEE J. Biomed. Health Informatics, March, 2024

Disentanglement of content and style features in multi-center cytology images via contrastive self-supervised learning.
Biomed. Signal Process. Control., 2024

2023
ParamNet: A Parameter-variable Network for Fast Stain Normalization.
CoRR, 2023

2022
STSRNet: Self-Texture Transfer Super-Resolution and Refocusing Network.
IEEE Trans. Medical Imaging, 2022

Cervical cytopathology image refocusing via multi-scale attention features and domain normalization.
Medical Image Anal., 2022

Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Data.
CoRR, 2022

2021
An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Features using Deep Learning.
CoRR, 2021

2020
PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network.
IEEE Trans. Medical Imaging, 2020

Reconstruct high-resolution multi-focal plane images from a single 2D wide field image.
CoRR, 2020

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides.
CoRR, 2020

2019
From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.
IEEE Trans. Medical Imaging, 2019

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.
Frontiers Neuroinformatics, 2019

Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization.
CoRR, 2019

2016
Large-scale localization of touching somas from 3D images using density-peak clustering.
BMC Bioinform., 2016


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