Shuai Lu

Orcid: 0000-0002-3532-7498

Affiliations:
  • Beijing University of Chemical Technology, Department of Mathematics, Beijing, China


According to our database1, Shuai Lu authored at least 13 papers between 2019 and 2024.

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

Timeline

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Bibliography

2024
Encoder-Decoder Contrast for Unsupervised Anomaly Detection in Medical Images.
IEEE Trans. Medical Imaging, March, 2024

Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder.
IEEE Trans. Image Process., 2024

2023
GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.
Medical Image Anal., December, 2023

E-Net: a novel deep learning framework integrating expert knowledge for glaucoma optic disc hemorrhage segmentation.
Multim. Tools Appl., November, 2023

PKRT-Net: Prior knowledge-based relation transformer network for optic cup and disc segmentation.
Neurocomputing, June, 2023

ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges.
CoRR, 2022

2021
A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis.
npj Digit. Medicine, 2021

2020
REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.
Medical Image Anal., 2020

A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect Segmentation.
IEEE Access, 2020

Classification and Recognition of Space Debris and Its Pose Estimation Based on Deep Learning of CNNs.
Proceedings of the HCI International 2020 - Posters - 22nd International Conference, 2020

2019
Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples.
Sensors, 2019

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs.
CoRR, 2019


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