Search:
Match:
5 results
Research Paper#Medical Imaging, Deep Learning, MRI Reconstruction🔬 ResearchAnalyzed: Jan 3, 2026 16:13

Motion-Resolved MRI Reconstruction with Deep Learning

Published:Dec 29, 2025 02:29
1 min read
ArXiv

Analysis

This paper addresses the challenge of respiratory motion artifacts in MRI, a significant problem in abdominal and pulmonary imaging. The authors propose a two-stage deep learning approach (MoraNet) for motion-resolved image reconstruction using radial MRI. The method estimates respiratory motion from low-resolution images and then reconstructs high-resolution images for each motion state. The use of an interpretable deep unrolled network and the comparison with conventional methods (compressed sensing) highlight the potential for improved image quality and faster reconstruction times, which are crucial for clinical applications. The evaluation on phantom and volunteer data strengthens the validity of the approach.
Reference

“The MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time.”

PermalinkArXiv
Paper#Medical AI🔬 ResearchAnalyzed: Jan 3, 2026 19:47

AI for Early Lung Disease Detection

Published:Dec 27, 2025 16:50
1 min read
ArXiv

Analysis

This paper is significant because it explores the application of deep learning, specifically CNNs and other architectures, to improve the early detection of lung diseases like COVID-19, lung cancer, and pneumonia using chest X-rays. This is particularly impactful in resource-constrained settings where access to radiologists is limited. The study's focus on accuracy, precision, recall, and F1 scores demonstrates a commitment to rigorous evaluation of the models' performance, suggesting potential for real-world diagnostic applications.
Reference

“The study highlights the potential of deep learning methods in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays.”

PermalinkArXiv
Research Paper#Medical AI, Audio Processing, Deep Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:25

Geometry-Aware Optimization Improves Respiratory Sound Classification

Published:Dec 27, 2025 11:39
1 min read
ArXiv

Analysis

This paper addresses the challenges of respiratory sound classification, specifically the limitations of existing datasets and the tendency of Transformer models to overfit. The authors propose a novel framework using Sharpness-Aware Minimization (SAM) to optimize the loss surface geometry, leading to better generalization and improved sensitivity, which is crucial for clinical applications. The use of weighted sampling to address class imbalance is also a key contribution.
Reference

“The method achieves a state-of-the-art score of 68.10% on the ICBHI 2017 dataset, outperforming existing CNN and hybrid baselines. More importantly, it reaches a sensitivity of 68.31%, a crucial improvement for reliable clinical screening.”

PermalinkArXiv
Research#Respiratory Signals🔬 ResearchAnalyzed: Jan 10, 2026 10:53

Novel Framework Enhances Respiratory Signal Analysis from Video

Published:Dec 16, 2025 05:04
1 min read
ArXiv

Analysis

This research focuses on improving the quality of respiratory signals derived from video analysis, a significant step towards non-invasive health monitoring. The development of such a framework could lead to more reliable and accessible diagnostic tools.
Reference

“The article's context indicates it is from ArXiv.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:45

Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals

Published:Nov 29, 2025 17:15
1 min read
ArXiv

Analysis

This article describes research on using explainable multi-modal deep learning to detect lung diseases from respiratory audio signals. The focus is on the explainability of the AI model, which is crucial for medical applications. The use of multi-modal data (likely combining audio with other data) suggests a potentially more robust and accurate diagnostic tool. The source, ArXiv, indicates this is a pre-print or research paper.
Reference

“”

PermalinkArXiv