7 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…
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…
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…
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…
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…
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…