2 citations · 3 across the 7 of their papers we have counts for
9 papers
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Joy Dhar, Manish Kumar Pandey, Nayyar Zaidi +4
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. Ho…
LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction
Ao Li, Chen Chen, Zhenyu Wang +3
Exposure correction is essential for enhancing image quality under challenging lighting conditions. While supervised learning has achieved significant progress in this area, it rel…
SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality
Sijie Li, Chen Chen, Jungong Han
In this paper, we propose SimMLM, a simple yet powerful framework for multimodal learning with missing modalities. Unlike existing approaches that rely on sophisticated network arc…
Missing-Modality-Aware Graph Neural Network for Cancer Classification
Sina Tabakhi, Chen, Haiping Lu
A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some patients. Existing approaches either e…
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…
Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment
Zeju Li, Ying-Qiu Zheng, Chen Chen +1
Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data. In such scenarios, the generated pseudo-labels tend to exhibit a bias towa…
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…