1 citations · 1 across the 10 of their papers we have counts for
11 papers
SelfCue: Making a 3D CT Report Generator Say What It Already Knows
Renjie Liang, Yang Yang, Jinqian Pan +3
Progress in 3D CT report generation is usually sought in increasingly sophisticated architectures and larger pools of training data. We find instead that a 3D CT report generator a…
ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
Renjie Liang, Zijian Xu, Jinqian Pan +6
A 3D CT scan entering a vision-language model produces a long sequence of visual tokens, often thousands to tens of thousands per volume, and this sequence must be compressed befor…
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…
Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
Renjie Liang, Zhengkang Fan, Jinqian Pan +4
Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT…
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…
Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities
Chengkun Sun, Jinqian Pan, Zhuoli Jin +3
Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities, consistent deactivation of nodes leading…