10 citations · 19 across the 14 of their papers we have counts for
15 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…
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…
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
Jiayang Zhang, Xianyuan Liu, Wei Wu +6
Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties. Understanding their stoichiometry, the number of protein subunits to for…
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…
Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
Prasun C Tripathi, Sina Tabakhi, Mohammod N I Suvon +5
Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered…
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
Mohammod N. I. Suvon, Prasun C. Tripathi, Wenrui Fan +7
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. card…
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…
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
Prasun C. Tripathi, Mohammod N. I. Suvon, Lawrence Schobs +4
Heart failure is a serious and life-threatening condition that can lead to elevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure (PAWP) is an important surroga…
Channel-Temporal Attention for First-Person Video Domain Adaptation
Xianyuan Liu, Shuo Zhou, Tao Lei +1
Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recogniti…
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Haiping Lu, Xianyuan Liu, Robert Turner +5
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundar…
Team PyKale (xy9) Submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition
Xianyuan Liu, Raivo Koot, Shuo Zhou +2
This report describes the technical details of our submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition. The EPIC-Kitchens dataset i…
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…
Domain Independent SVM for Transfer Learning in Brain Decoding
Shuo Zhou, Wenwen Li, Christopher R. Cox +1
Brain imaging data are important in brain sciences yet expensive to obtain, with big volume (i.e., large p) but small sample size (i.e., small n). To tackle this problem, transfer…
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Wenwen Li, Jian Lou, Shuo Zhou +1
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional st…