This page documents the open source projects created by, or receive significant contributions from, lab members.

Contact: yanye.lu@pku.edu.cn

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NI‐P3D‐Unet OCTA

Date Created
20220324

Owner
Zhe Jiang

Implement in
PyTorch

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
TBD

Data
Coming soon

License
TBD (all rights researved)

Related Paper
DOI

Weakly Supervised DL-OCTA

Date Created
20220324

Owner
Zhe Jiang

Implement in
PyTorch

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
TBD

Data
Coming soon

License
TBD (all rights researved)

Related Paper
DOI

BLRM: Bayesian Statistics Guided Label Refurbishment Mechanism

Date Created
20220321

Owner
Mengdi Gao

Implement in
PyTorch

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. 

A manuscript has been submitted to submit to Medical Physics, and now is under revision..

Repository
GitHub

License
TBD (all rights researved)

Related Paper
DOI 

DA-WSOL

Date Created
20220308

Owner
Lei Zhu

Implement in
PyTorch

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
GitHub

License
TBD (all rights researved)

Related Paper
arxiv

TCFL_Unsupervised_OCT_Denoising

Date Created
20211210

Owner
Mufeng Geng

Implement in
PyTorch

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
Github (expect in Autumn)

Data
Coming soon

License
TBD (all rights researved)

Related Paper
DOI 

Spectral Nonlocal Block

Date Created
20210803

Owner
Lei Zhu

Implement in
PyTorch

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
GitHub

License
TBD (all rights researved)

Related Paper
arxiv

CNCL_Medical_Image_Denoising

Date Created
20210618

Owner
Mufeng Geng

Implement in
PyTorch

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
Github

License
TBD (all rights researved)

Related Paper
DOI

PMRMC

Date Created
20200802

Owner
Xiangxi Meng

Contribute by
Lujia Jin

Implement in
MATLAB

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
Github

License
TBD (all rights researved)

Related Paper
DOI

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