Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Deep Learning-Based Diagnosis Of Disease Activity In Patients With Graves’ Orbitopathy Using Orbital Spect/Ct, Ni Yao, Longxi Li, Zhengyuan Gao, Chen Zhao, Yanting Li, Chuang Han, Jiaofen Nan, Zelin Zhu, Yi Xiao, Fubao Zhu, Min Zhao, Weihua Zhou
Deep Learning-Based Diagnosis Of Disease Activity In Patients With Graves’ Orbitopathy Using Orbital Spect/Ct, Ni Yao, Longxi Li, Zhengyuan Gao, Chen Zhao, Yanting Li, Chuang Han, Jiaofen Nan, Zelin Zhu, Yi Xiao, Fubao Zhu, Min Zhao, Weihua Zhou
Michigan Tech Publications
Purpose: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO …
A Few-Shot Learning Model Based On A Triplet Network For The Prediction Of Energy Coincident Peak Days, Jinxiang Liu, Laura Brown
A Few-Shot Learning Model Based On A Triplet Network For The Prediction Of Energy Coincident Peak Days, Jinxiang Liu, Laura Brown
Michigan Tech Publications
In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand. Advanced information about the time of coincident peaks would be helpful for both utility companies and their customers. This work addresses the prediction of the five coincident peak days (5CP) in a year. We present a few-shot learning model to classify a day as a 5CP day or a non-5CP day 24-hours ahead. A triplet network is implemented for the 2-way-5-shot classifications on six different historical …
Regional Scale Dryland Vegetation Classification With An Integrated Lidar-Hyperspectral Approach, Hamid Dashti, Andrew Poley, Nancy Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Et. Al.
Regional Scale Dryland Vegetation Classification With An Integrated Lidar-Hyperspectral Approach, Hamid Dashti, Andrew Poley, Nancy Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Et. Al.
Michigan Tech Publications
The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral …
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Michigan Tech Publications
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …