6 papers
An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification
Chengkun Sun, Jinqian Pan, Renjie Liang +7
Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and trea…
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
Di Xu, Hengjie Liu, Yang Yang +11
Accelerated dynamic volumetric magnetic resonance imaging (4DMRI) is essential for applications relying on motion resolution. Existing 4DMRI produces acceptable artifacts of averag…
DTC: A Deformable Transposed Convolution Module for Medical Image Segmentation
Chengkun Sun, Jinqian Pan, Renjie Liang +4
In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones, enabling feature fusion bet…
MedSAM-based lung masking for multi-label chest X-ray classification
Brayden Miao, Zain Rehman, Xin Miao +2
Chest X-ray (CXR) imaging is widely used for screening and diagnosing pulmonary abnormalities, yet automated interpretation remains challenging due to weak disease signals, dataset…
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations
Di Xu, Hengjie Liu, Xin Miao +9
The scanning time for a fully sampled MRI can be undesirably lengthy. Compressed sensing has been developed to minimize image artifacts in accelerated scans, but the required itera…
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement
Di Xu, Xin Miao, Hengjie Liu +8
Abstract Purpose: High-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory phases. Accelerated sparse sampling…