Search Results for author:
Found 23 papers, 8 papers with code
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives
In cancer therapeutics, protein-metal binding mechanisms critically govern drug pharmacokinetics and targeting efficacy, thereby fundamentally shaping the rational design of anticancer metallodrugs.
Towards deployment-centric multimodal AI beyond vision and language
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering.
Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment
Several studies in PH have applied machine learning to low-cost diagnostic tools like 12-lead ECG (12L-ECG), but they mainly focus on areas with limited resources, overlooking areas with no diagnostic tools, such as rural primary healthcare in LMICs.
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction
To address the challenge of data scarcity, we propose FMM$_{TC}$, a Foundation-Model-boosted Multimodal learning framework for fMRI-based neuropathic pain drug response prediction, which leverages both internal multimodal information in pain-specific data and external knowledge from large pain-agnostic data.
Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
Virus-like particles (VLPs) are valuable for vaccine development due to their immune-triggering properties.
Quantum Neural Network for Accelerated Magnetic Resonance Imaging
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features.
Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications.
Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network
Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models.
Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
We extract spatio-temporal features from CMR scans using tensor-based learning.
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X, G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset.
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
In this work, we develop a tensor learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic Resonance Imaging (MRI).
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
We present a brand-new and general machine learning method for material property prediction.
Channel-Temporal Attention for First-Person Video Domain Adaptation
Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures.
A Behavior-aware Graph Convolution Network Model for Video Recommendation
Interactions between users and videos are the major data source of performing video recommendation.
Team PyKale (xy9) Submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition
Under the team name xy9, our submission achieved 5th place in terms of top-1 accuracy for verb class and all top-5 accuracies.
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
Domain Independent SVM for Transfer Learning in Brain Decoding
We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks.
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available.
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
Ranked #41 on
Image Classification
on mini WebVision 1.0
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT.
