2 citations · 3 across the 7 of their papers we have counts for
7 papers
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
Xiaokun Liu, Sayedmohammadreza Rastegari, Yijun Huang +12
In cancer therapeutics, protein-metal binding mechanisms critically govern the pharmacokinetics and targeting efficacy of drugs, thereby fundamentally shaping the rational design o…
Towards deployment-centric multimodal AI beyond vision and language
Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…
Multimodal Latent Fusion of ECG Leads for Early Assessment of Pulmonary Hypertension
Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi +7
Recent advancements in early assessment of pulmonary hypertension (PH) primarily focus on applying machine learning methods to centralized diagnostic modalities, such as 12-lead el…
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction
Wenrui Fan, L. M. Riza Rizky, Jiayang Zhang +5
Neuropathic pain, affecting up to 10% of adults, remains difficult to treat due to limited therapeutic efficacy and tolerability. Although resting-state functional MRI (rs-fMRI) is…
Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network
Shuo Zhou, Junhao Luo, Yaya Jiang +3
Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typica…
MeDSLIP: Medical Dual-Stream Language-Image Pre-training with Pathology-Anatomy Semantic Alignment
Wenrui Fan, Mohammod N. I. Suvon, Shuo Zhou +6
Pathology and anatomy are two essential groups of semantics in medical data. Pathology describes what the diseases are, while anatomy explains where the diseases occur. They descri…
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
Markus D. Schirmer, Archana Venkataraman, Islem Rekik +23
Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question re…