activity
20172024
most citedTrainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT

20 citations · 31 across the 8 of their papers we have counts for

collaborators

8 papers

eess.IV20241 cited

EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction

Yipeng Sun, Yixing Huang, Linda-Sophie Schneider +5

Computed Tomography (CT) image reconstruction is crucial for accurate diagnosis and deep learning approaches have demonstrated significant potential in improving reconstruction qua…

eess.IV20241 cited

Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization

Chang Liu, Laura Klein, Yixing Huang +4

To facilitate a prospective estimation of CT effective dose and risk minimization process, a prospective spatial dose estimation and the known anatomical structures are expected. T…

cs.LG20231 cited

A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration

Yixing Huang, Christoph Bert, Ahmed Gomaa +3

Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter…

cs.CV20232 cited

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

Zhisheng Wang, Zihan Deng, Fenglin Liu +3

Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojec…

eess.IV20233 cited

The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning

Florian Putz, Johanna Grigo, Thomas Weissmann +13

Background: Tumor segmentation in MRI is crucial in radiotherapy (RT) treatment planning for brain tumor patients. Segment anything (SA), a novel promptable foundation model for au…

cs.CV2023

Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning

Philipp Sommer, Yixing Huang, Christoph Bert +5

Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imagi…