Xue, Zhihao Zhu, Sicheng Yang, Fan Gao, Juan Peng, Hao Zou, Chao Jin, Hang Hu, Chenxi
Published in
Frontiers in Cardiovascular Medicine
Introduction High-resolution whole-heart coronary magnetic resonance angiography (CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration highly desirable. Traditional reconstruction methods used in CMRA rely on either hand-crafted priors or supervised learning models. Although the latter often yield superior reconstru...
Yao, Bin Jin, Lujia Hu, Jiakui Liu, Yuzhao Yan, Yuepeng Li, Qing Lu, Yanye
Published in
Physics in Medicine & Biology
Objective. Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining...
Loutati, Ranel Kolben, Yotam Luria, David Amir, Offer Biton, Yitschak
Published in
Frontiers in Cardiovascular Medicine
Background The traditional classification of left ventricular hypertrophy (LVH), which relies on left ventricular geometry, fails to correlate with outcomes among patients with increased LV mass (LVM). Objectives To identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association wi...
Marc, Brice Foucher, Philippe Forbes, Florence Charbonnier, Pierre
Automatic anomaly detection on engineering structures is often carried out using supervised models, raising the issue of anomalous images acquisition and annotation. Unsupervised methods like normalizing flows achieve excellent results while trained with defect-free images only. However, normalizing flows methods, such as MSFlow, are generally appl...
Published in
Frontiers in Artificial Intelligence
The world urgently needs new sources of clean energy due to a growing global population, rising energy use, and the effects of climate change. Nuclear energy is one of the most promising solutions for meeting the world’s energy needs now and in the future. One type of nuclear energy, Low Energy Nuclear Reactions (LENR), has gained interest as a pot...
O’Shea, Finn H Joung, Semin Smith, David R Ratner, Daniel Coffee, Ryan
Published in
Machine Learning: Science and Technology
Using supervised learning to train a machine learning model to predict an on-coming edge localized mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consuming process for a human. Considering this need and...
Wei, Ran Li, ZhengYang Geng, Lei Wuken, Muheiti Liu, YanBei
Published in
Measurement Science and Technology
To address the issue of false positive (FP) detections in image anomaly detection caused by the loss of low-frequency features when dealing with high-dimensional feature distributions, we propose the multi-layer Gaussian discriminant anomaly detection model (MGAD). This model utilizes distance metrics based on multiple normal distributions to perfo...
Rana, Neeta Marwaha, Hitesh
Published in
E3S Web of Conferences
The World Health Organization recognizes pneumonia as a significant global health issue. Artificial intelligence, particularly machine learning, and deep learning has emerged as valuable tools for improving pneumonia diagnosis. However, these techniques face a major challenge: the lack of labeled data. To tackle this, we propose using unsupervised ...
Humble, Ryan Zhang, Zhe O’Shea, Finn Darve, Eric Ratner, Daniel
Published in
Machine Learning: Science and Technology
Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexiste...
Bai, Dongxu Li, Gongfa Jiang, Du Jiang, Guozhang Hao, Zhiqiang Zhou, Dalin Ju, Zhaojie
Published in
Measurement Science and Technology
Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sa...
