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20242026
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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2024

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…

cs.CV2024

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…