1 citations · 1 across the 5 of their papers we have counts for
7 papers
OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis
Tienyu Chang, Zhen Chen, Renjie Liang +9
The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representations from large-scale unlabeled…
Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation
Renjie Liang, Yiling Ma, Yang Xing +6
Automated radiology report generation from 3D CT volumes often suffers from incomplete pathology coverage. We provide empirical evidence that this limitation stems from a represent…
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…
A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation
Renjie Liang, Zhengkang Fan, Jinqian Pan +4
Objective Renal cancer is a common malignancy and a major cause of cancer-related deaths. Computed tomography (CT) is central to early detection, staging, and treatment planning. H…
GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation
Chengkun Sun, Russell Stevens Terry, Jiang Bian +1
Accurate segmentation of multiple organs and the differentiation of pathological tissues in medical imaging are crucial but challenging, especially for nuanced classifications and…
BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models
Chengkun Sun, Jinqian Pan, Russell Stevens Terry +2
Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically…