IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

no code implementations • 18 Jun 2024 • Min Li, Chen Chen, Zhuang Xiong, Ying Liu, Pengfei Rong, Shanshan Shan, Feng Liu, Hongfu Sun, Yang Gao

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases.

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

1 code implementation • 14 Nov 2023 • Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun

The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset.

DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

1 code implementation • 9 Jul 2023 • Liangrui Pan, Dazhen Liu, Yutao Dou, Lian Wang, Zhichao Feng, Pengfei Rong, Liwen Xu, Shaoliang Peng

In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning to analyze cancer multi-omics data for the identification and characterization of cancer subtypes.

Contrastive Learning