activity
20172026
most citedMedical Image Segmentation Using Squeeze-and-Expansion Transformers

24 citations · 30 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Confidence-Adaptive SwiGLU for Mixture-of-Experts

Shaohua Li, Xiuchao Sui, Xiaobing Sun +4

SwiGLU has become a standard gated activation in modern Transformer MLPs, yet its gate sharpness -- the smoothness and selectivity of the gating function -- is typically fixed thro…

cs.CL2026

Structured Semantic Cloaking for Jailbreak Attacks on Large Language Models

Xiaobing Sun, Perry Lam, Shaohua Li +4

Modern LLMs employ safety mechanisms that extend beyond surface-level input filtering to latent semantic representations and generation-time reasoning, enabling them to recover obf…

eess.IV2022

Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks

Xiaofeng Lei, Shaohua Li, Xinxing Xu +9

Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elus…

eess.IV2022

REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

Huihui Fang, Fei Li, Junde Wu +25

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there ar…

cs.CV2021

Few-Shot Domain Adaptation with Polymorphic Transformers

Shaohua Li, Xiuchao Sui, Jie Fu +7

Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the train…

eess.IV2021★ 24 cited

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

Shaohua Li, Xiuchao Sui, Xiangde Luo +3

Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn ima…

eess.IV2019★ 2 cited

Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing

Shaohua Li, Ayesha Anees, Yu Zhong +4

Eddy current testing (ECT) is one of the most popular Nondestructive Testing (NDT) techniques, especially for conductive materials. Reconstructing the crack profile from measured E…

cs.CV2017★ 4 cited

Enhanced Attacks on Defensively Distilled Deep Neural Networks

Yujia Liu, Weiming Zhang, Shaohua Li +1

Deep neural networks (DNNs) have achieved tremendous success in many tasks of machine learning, such as the image classification. Unfortunately, researchers have shown that DNNs ar…