Publications

A collection of my research work.

Glance Before You Tell: Anomaly-Guided 3D Radiology Report Generation with Heat-Conduction Slice Encoders

Glance Before You Tell: Anomaly-Guided 3D Radiology Report Generation with Heat-Conduction Slice Encoders

Lingyu Zhou, Dingwen Pi, Zhang Yi, Jun Wu, Xiuyuan Xu

Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) 2026 · CCF-A · Poster

Proposed HeatRAD, a glance-before-tell framework that routes anomaly-guided slice evidence into an organ-ordered visual prefix for 3D radiology report generation.
Mitigating Entity Hallucinations in 3D Radiology Report Generation via Dual-Stream Alignment

Mitigating Entity Hallucinations in 3D Radiology Report Generation via Dual-Stream Alignment

Lingyu Zhou†, Yue Yu†, Zhang Yi, Xiuyuan Xu

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2026 · CCF-A · Poster

Proposed DEAR framework to mitigate entity hallucinations in 3D radiology report generation through dual-stream alignment.
Code
ForeMatch: Rethinking Foreground Consistency for Efficient Semi-Supervised 3D Medical Image Segmentation

ForeMatch: Rethinking Foreground Consistency for Efficient Semi-Supervised 3D Medical Image Segmentation

Lingyu Zhou, Zhengyang Xu, Zhang Yi, Deng Xiong, Xiuyuan Xu

Proceedings of the ACM International Conference on Multimedia (ACM MM) 2026 · CCF-A · Poster

Proposed ForeMatch for efficient semi-supervised 3D medical image segmentation via foreground consistency.
Code
Navigate Before You Generate: Volume Evidence Grounding for 3D Radiology Reports

Navigate Before You Generate: Volume Evidence Grounding for 3D Radiology Reports

Lingyu Zhou, Dingwen Pi, Jun Wu, Zhang Yi, Xiuyuan Xu

IEEE Transactions on Image Processing (IEEE TIP) 2026 · CCF-A · IF=13.7 · Under Review

Proposed Vol-Nav, a volume evidence grounding paradigm for 3D radiology report generation.
Prospective Multicenter Validation of a Self-Supervised Deep Learning Model for Predicting Lung Cancer Invasiveness in Pulmonary Nodular Ground Glass Opacities

Prospective Multicenter Validation of a Self-Supervised Deep Learning Model for Predicting Lung Cancer Invasiveness in Pulmonary Nodular Ground Glass Opacities

Ruichen Cui†, Xiuyuan Xu†, Nan Chen†, Lingyu Zhou†, others

BMJ Journals - Thorax 2026 · IF=8.3 · Under Review

Self-supervised learning framework for predicting lung cancer invasiveness in GGOs via prospective multicenter validation.
Efficient and Gender-Adaptive Graph Vision Mamba for Pediatric Bone Age Assessment

Efficient and Gender-Adaptive Graph Vision Mamba for Pediatric Bone Age Assessment

Lingyu Zhou, Zhang Yi, Kai Zhou, Xiuyuan Xu

Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2024 · CCF-B · Poster

Proposed GGVMamba, a gender-adaptive Graph Vision Mamba framework for pediatric bone age assessment.
Code
A Bayesian Deep Learning Model with Consolidation-to-Tumor Ratio (CTR) Prior Revolutionizes the Prediction of Spread Through Air Spaces (STAS) in Stage IA Lung Adenocarcinoma: A Large-Scale Diagnostic Study

A Bayesian Deep Learning Model with Consolidation-to-Tumor Ratio (CTR) Prior Revolutionizes the Prediction of Spread Through Air Spaces (STAS) in Stage IA Lung Adenocarcinoma: A Large-Scale Diagnostic Study

Jie Cao†, Nan Chen†, Lingyu Zhou†, others

Translational Lung Cancer Research 2025 · IF=3.5

Developed a Bayesian deep learning model with CTR prior for predicting STAS in lung adenocarcinoma.

Deep Learning for Precise Diagnosis and Subtype Triage of Drug-Resistant Tuberculosis on Chest Computed Tomography

Shufan Liang, Xiuyuan Xu, Zhe Yang, Qiuyu Du, Lingyu Zhou, others

MedComm 2024 · IF=10.7

Deep learning framework for drug-resistant tuberculosis diagnosis and subtype triage from chest CT.

LTS-NET: Lung Tissue Segmentation from CT Images using Fully Convolutional Neural Network

Lingyu Zhou, Xiuyuan Xu, Kai Zhou, Jixiang Guo

11th International Conference on Information Science and Technology (ICIST) 2021

Proposed LTS-NET for automated lung tissue segmentation from CT images using FCN.