MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses

2020 ◽  
pp. 323-368
Author(s):  
Marco C. Pinho ◽  
Kaustav Bera ◽  
Niha Beig ◽  
Pallavi Tiwari
Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.


Author(s):  
Ashley Ghiaseddin ◽  
Lan B Hoang Minh ◽  
Michalina Janiszewska ◽  
David Shin ◽  
Wolfgang Wick ◽  
...  

Abstract Despite therapeutic advances for other malignancies, gliomas remain challenging solid tumors to treat. Complete surgical resection is nearly impossible due to gliomas’ diffuse infiltrative nature, and treatment is hampered by restricted access to the tumors due to limited transport across the blood-brain barrier (BBB). Recent advances in genomic studies and next-generation sequencing techniques have led to a better understanding of gliomas and identification of potential aberrant signaling pathways. Targeting the specific genomic abnormalities via novel molecular therapies has opened a new avenue in the management of gliomas, with encouraging results in preclinical studies and early clinical trials. However, molecular characterization of gliomas revealed the significant heterogeneity, which poses a challenge for targeted therapeutic approaches. In this context, leading neuro-oncology researchers and clinicians, industry innovators, and patient advocates convened at the inaugural annual Remission Summit held in Orlando, FL in February 2019 to discuss the latest advances in immunotherapy and precision medicine approaches for the treatment of adult and pediatric brain tumors and outline the unanswered questions, challenges, and opportunities that lay ahead for advancing the duration and quality of life for patients with brain tumors. Here, we provide historical context for precision medicine in other cancers, present emerging approaches for gliomas, discuss their limitations, and outline the steps necessary for future success. We focus on the advances in small molecule targeted therapy, as the use of immunotherapy as an emerging precision medicine modality for glioma treatment has recently been reviewed by our colleagues


2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.


2020 ◽  
Vol 40 (3) ◽  
pp. 1225-1232 ◽  
Author(s):  
Raheleh Hashemzehi ◽  
Seyyed Javad Seyyed Mahdavi ◽  
Maryam Kheirabadi ◽  
Seyed Reza Kamel

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