Machine learning to identify suitable boundaries for band-pass spectral analysis of dynamic [ $$^{11}$$ 11 C]Ro15-4513 PET scan and voxel-wise parametric map generation by Zeyu Chang, Colm J. McGinnity, Rainer Hinz, Manlin Wang, Joel Dunn, Ruoyang Liu, Mubaraq Yakubu, Paul Marsden, Alexander Hammers

“…The process currently requires the manual selection of frequency ranges based on the data. To enhance the efficiency of band-pass spectral analysis and extend its application to a broader range of tracers, we propose employing machine learning to automate the selection of spectral boundaries. …”
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Construction and scheduling optimization of renewable energy consumption forecasting system for twisted tire porcelain manufacturing industry based on deep learning by Fangrong Liao, Runlin Ran, Jiayi Zhang

“…At the same time, based on the convolutional neural network model Alexnet, the cross-entropy loss function is selected as the training loss, and the twisted tire porcelain pattern classification algorithm under the deep learning framework is designed. …”
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Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty–guided space-time analysis... by Dilek M. Yalcinkaya, MS, Khalid Youssef, PhD, Bobak Heydari, MD, Janet Wei, MD, C. Noel Bairey Merz, MD, Robert Judd, PhD, Rohan Dharmakumar, PhD, Orlando P. Simonetti, PhD, Jonathan W. Weinsaft, MD, Subha V. Raman, MD, Behzad Sharif, PhD

“…In our approach, dubbed data-adaptive uncertainty–guided space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the “best” one among the pool of solutions. …”
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