Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data
The research explores the utilization of a deep learning model employing an attention
mechanism in medical text mining. It targets the challenge of analyzing unstructured text …
mechanism in medical text mining. It targets the challenge of analyzing unstructured text …
[HTML][HTML] Few-shot and meta-learning methods for image understanding: a survey
State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …
An accelerated doubly stochastic gradient method with faster explicit model identification
Sparsity regularized loss minimization problems play an important role in various fields
including machine learning, data mining, and modern statistics. Proximal gradient descent …
including machine learning, data mining, and modern statistics. Proximal gradient descent …
Exploiting partial common information microstructure for multi-modal brain tumor segmentation
Y Mei, G Venkataramani, T Lan - Workshop on Machine Learning for …, 2023 - Springer
Learning with multiple modalities is crucial for automated brain tumor segmentation from
magnetic resonance imaging data. Explicitly optimizing the common information shared …
magnetic resonance imaging data. Explicitly optimizing the common information shared …
A learnable variational model for joint multimodal MRI reconstruction and synthesis
Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information
but is limited in practice due to excessive data acquisition time. In this paper, we propose a …
but is limited in practice due to excessive data acquisition time. In this paper, we propose a …
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning
W Bian - arXiv preprint arXiv:2406.02626, 2024 - arxiv.org
Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and
high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep …
high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep …
Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks
Aiming at the limitations of traditional medical decision system in processing large-scale
heterogeneous medical data and realizing highly personalized recommendation, this paper …
heterogeneous medical data and realizing highly personalized recommendation, this paper …
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser
Y Li, Y Duan - arXiv preprint arXiv:2211.07286, 2022 - arxiv.org
Due to the development of deep learning-based denoisers, the plug-and-play strategy has
achieved great success in image restoration problems. However, existing plug-and-play …
achieved great success in image restoration problems. However, existing plug-and-play …
Check for updates Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation
Y Mei, G Venkataramani, T Lan - Machine Learning for …, 2023 - books.google.com
Learning with multiple modalities is crucial for automated brain tumor segmentation from
magnetic resonance imaging data. Explicitly optimizing the common information shared …
magnetic resonance imaging data. Explicitly optimizing the common information shared …
A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner
W Bian - arXiv preprint arXiv:2406.17804, 2024 - arxiv.org
This paper presents a comprehensive analysis of both conventional and deep learning
methods for eliminating electromagnetic interference (EMI) in MRI systems. We explore the …
methods for eliminating electromagnetic interference (EMI) in MRI systems. We explore the …