activity
20242026
most citedMLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation

1 citations · 1 across the 8 of their papers we have counts for

collaborators

9 papers

cs.LG2026

ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction

Hexiao Ding, Hongzhao Chen, Jing Lan +12

Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pa…

cs.CV2026

DeepMoLM: Leveraging Visual and Geometric Structural Information for Molecule-Text Modeling

Jing Lan, Hexiao Ding, Hongzhao Chen +8

AI models for drug discovery and chemical literature mining must interpret molecular images and generate outputs consistent with 3D geometry and stereochemistry. Most molecular lan…

eess.AS2025

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

Hongzhao Chen, XiaoYang Wang, Jing Lan +9

Automatic speech recognition (ASR) in clinical dialogue demands robustness to full-duplex interaction, speaker overlap, and low-latency constraints, yet open benchmarks remain scar…

cs.LG2025

Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery

Jing Lan, Hexiao Ding, Hongzhao Chen +8

Accurate identification of drug-target interactions (DTI) remains a central challenge in computational pharmacology, where sequence-based methods offer scalability. This work intro…

q-bio.BM2025

Contrastive Multi-Task Learning with Solvent-Aware Augmentation for Drug Discovery

Jing Lan, Hexiao Ding, Hongzhao Chen +7

Accurate prediction of protein-ligand interactions is essential for computer-aided drug discovery. However, existing methods often fail to capture solvent-dependent conformational…

eess.IV2025

REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification

Hongzhao Chen, Hexiao Ding, Yufeng Jiang +8

Reliable and interpretable tumor classification from clinical imaging remains a core challenge. The main difficulties arise from heterogeneous modality quality, limited annotations…

eess.IV2025

Decoding the Alzheimer's Continuum: Interpretable Multi-Gate Routing for Diagnosis and Transition Prediction

Yufeng Jiang, Hexiao Ding, Hongzhao Chen +8

Alzheimer's disease (AD) manifests as a continuous progression from normal cognition (NC) through mild cognitive impairment (MCI) to dementia. However, most deep learning approache…

eess.IV2024★ 1 cited

MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation

Yufeng Jiang, Zongxi Li, Xiangyan Chen +2

Recent advancements in medical imaging have resulted in more complex and diverse images, with challenges such as high anatomical variability, blurred tissue boundaries, low organ c…

eess.IV2024

MoE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts

Yufeng Jiang, Yiqing Shen

Medical imaging data is inherently heterogeneous across different modalities and clinical centers, posing unique challenges for developing generalizable foundation models. Conventi…