This page documents the open source projects created by, or receive significant contributions from, lab members.
Contact: yanye.lu@pku.edu.cn
| Name | Infomation | Introduction | Access |
|---|---|---|---|
NI‐P3D‐Unet OCTA
| Date Created Owner Implement in | In this paper, we designed a neighborhood information‐fused Pseudo‐3D U‐Net (NI‐P3D‐U) for OCTA reconstruction. The proposed NI-P3D-U was investigated on an in vivo animal dataset by a cross-validation strategy under both fully supervised learning and weakly supervised learning pipelines. The related work has been published by Medical Physics. | Repository Data License Related Paper |
Weakly Supervised DL-OCTA
| Date Created Owner Implement in | In this paper, we proposed a weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels.The proposed pipeline was investigated on an in-vivo animal dataset and a human eye dataset by a crossvalidationstrategy. The related work has been published by IEEE Transactions on Medical Imaging. | Repository Data License Related Paper |
| BLRM: Bayesian Statistics Guided Label Refurbishment Mechanism | Date Created Owner Implement in | In this work, we proposed a novel Bayesian statistics guided label refurbishment mechanism (BLRM) for DNNs to prevent overftting noisy images. BLRM utilized maximum a posteriori probability (MAP) in the Bayesian statistics and the exponentially time-weighted technique to selectively correct the labels of noisy images. This work has been accepted by Medical Physics. | Repository License Related Paper |
DA-WSOL | Date Created Owner Implement in | DA-WSOL demonstrates the implementation of paper ``Weakly Supervised Object Localization as Domain Adaption". Our method views WSOL as adapting the image-level features and pixel-level features, which is respectively focused by training and testing process. Moreover, our work also provide an efficient pipeline to utilize DA methods for assisting different of WSOL methods by proposing a DAL loss and a target sample assigner. This work has been accepted by CVPR2022. | Repository License Related Paper |
TCFL_Unsupervised_OCT_Denoising | Date Created Owner Implement in | TCFL_Unsupervised_OCT_Denoising demonstrates the implementation of the Triplet Cross-Fusion Learning for Unsupervised Noise Reduction of OCT Images. The proposed TCFL strategy is able to utilize concise structure to effectively reduce speckle noise, instead of designing complex network structures which is time-consuming and requires extensive empirical knowledge. This work has been accepted by IEEE Transactions on Medical Imaging. | Repository Data License Related Paper |
Spectral Nonlocal Block
| Date Created Owner Implement in | The Spectral View of Non-local (SNL) provide a novel perspective for the model design of non-local blocks. Our spectral view can help to theoretically analyze exsiting non-local blocks and design novel non-local block with the help of graph signal processing (e.g. the graph neural networks). This work has been published by the Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV2021) | Repository License Related Paper |
CNCL_Medical_Image_Denoising
| Date Created Owner Implement in | CNCL_Medical_Image_Denoising demonstrates the implementation of the Content-Noise Complementary Learning for Medical Imaging Denoising. This denoising pipeline processes both the content and the noise in the denoising task, and has shown generalization ability in many types of medical imaging. This work has been accepted by IEEE Transactions on Medical Imaging. | Repository License Related Paper |
PMRMC
| Date Created Owner Contribute by Implement in | PMRMC is a Monte Carlo simulation of the transportation and annihilation process of positrons inside a uniform magnetic field. PMRMC was created by Xiangxi Meng in Beijing Cancer Hospital. Lujia Jin (PhD student in MILab) also made a significant contribution. The related work has been published by Medical Physics. | Repositroy License Related Paper |
Other open source projects
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