Fengbei Liu

Orcid: 0000-0003-0355-2006

According to our database1, Fengbei Liu authored at least 29 papers between 2020 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning.
IEEE Trans. Medical Imaging, January, 2024

2023
Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images.
Medical Image Anal., December, 2023

Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable Image Classification.
CoRR, 2023

Generative Noisy-Label Learning by Implicit Dicriminative Approximation with Partial Label Prior.
CoRR, 2023

A Closer Look at Audio-Visual Semantic Segmentation.
CoRR, 2023

BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations.
CoRR, 2023

Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning.
CoRR, 2023

Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder.
Proceedings of the Machine Learning in Medical Imaging - 14th International Workshop, 2023

Learning Support and Trivial Prototypes for Interpretable Image Classification.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation.
CoRR, 2022

Translation Consistent Semi-supervised Segmentation for 3D Medical Images.
CoRR, 2022

Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder.
CoRR, 2022

Semantic-guided Image Virtual Attribute Learning for Noisy Multi-label Chest X-ray Classification.
CoRR, 2022

Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

NVUM: Non-volatile Unbiased Memory for Robust Medical Image Classification.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes.
Proceedings of the Computer Vision - ECCV 2022, 2022

ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification.
CoRR, 2021

Multi-centred Strong Augmentation via Contrastive Learning for Unsupervised Lesion Detection and Segmentation.
CoRR, 2021

Noisy Label Learning for Large-scale Medical Image Classification.
CoRR, 2021

Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification.
Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

3D Semantic Mapping from Arthroscopy Using Out-of-Distribution Pose and Depth and In-Distribution Segmentation Training.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020
Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning.
IEEE Access, 2020

Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020


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