activity
20242026
most citedNo Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation

2 citations · 2 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT

Shuo Jiang, Yuhao Hong, Chunbo Jiang +9

Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-t…

cs.CV2026

EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection

Shuo Jiang, Gaojia Zhang, Min Tan +2

Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings, as well as the relia…

cs.CV2025

A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation

Wenjing Yu, Shuo Jiang, Yifei Chen +9

Test time Adaptation is a promising approach for mitigating domain shift in medical image segmentation; however, current evaluations remain limited in terms of modality coverage, t…

cs.CV2025

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction

Shuo Jiang, Zhuwen Chen, Liaoman Xu +6

Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which reduces their usefulness in clinical…

cs.CV2025

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI

Yuanhan Wang, Yifei Chen, Shuo Jiang +7

Reliable brain tumor segmentation in MRI is indispensable for treatment planning and outcome monitoring, yet models trained on curated benchmarks often fail under domain shifts ari…

cs.CV2025★ 2 cited

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation

Shenghao Zhu, Yifei Chen, Weihong Chen +6

Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its ability to capture complementary tu…

cs.CV2025

Bridging the Gap in Missing Modalities: Leveraging Knowledge Distillation and Style Matching for Brain Tumor Segmentation

Shenghao Zhu, Yifei Chen, Weihong Chen +5

Accurate and reliable brain tumor segmentation, particularly when dealing with missing modalities, remains a critical challenge in medical image analysis. Previous studies have not…

math.CO2025

The enumeration of plane (3,6)-triangulations and the form of odd perfect numbers

Jan Florek

Let be the family of all -connected plane triangulations with vertices of degree or . The number of non-isomorphic triangulations belonging to $\math…

eess.IV2024

XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder

Shenghao Zhu, Yifei Chen, Shuo Jiang +8

Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magn…