Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography

2021 ◽  
Author(s):  
Dan Li ◽  
Rong Hu ◽  
Huizhou Li ◽  
Yeyu Cai ◽  
Paul J. Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Automatic Machine

Performance of Automatic Machine Learning versus Radiologists in the Evaluation of Endometrium on Computed Tomography

2020 ◽  
Author(s):  
Dan Li ◽  
Rong Hu ◽  
Jing Wu ◽  
Shixin Liu ◽  
Subhanik Purkayastha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Automatic Machine

scholarly journals Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learning

2020 ◽  
Author(s):  
Luciano Vinas ◽  
Jessica Scholey ◽  
Martina Descovich ◽  
Vasant Kearney ◽  
Atchar Sudhyadhom
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Megavoltage Computed Tomography

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

2021 ◽  
Vol 18 ◽  
Author(s):  
Nosaiba Al-Ryalat ◽  
Lna Malkawi ◽  
Ala'a Abu Salhiyeh ◽  
Faisal Abualteen ◽  
Ghaida Abdallah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Nuclear Medicine ◽  
Medical Imaging ◽  
Core Collection ◽  
Web Of Science ◽  
Vos Viewer ◽  
Literature Mapping ◽  
Link Strength ◽  
Linkage Strength

Objectives: Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020, analyzing the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping, along with a bibliometric analysis for publications. Methods: We performed a search on Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, which represents major topics discussed. Of the resulting topics, literature map created, and linkage analysis done. Results: A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords, only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while “computed tomography” has the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by “radiotherapy, and “deep and machine learning”. The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included “COVID-19” or “coronavirus” in their title, including the top cited paper. Conclusion: While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represent the topic of top cited articles in 2020 radiology publications.

A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

2021 ◽  
Author(s):  
Giovanni Capretti ◽  
Cristiana Bonifacio ◽  
Crescenzo De Palma ◽  
Martina Nebbia ◽  
Caterina Giannitto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Risk Model ◽  
Postoperative Outcomes ◽  
Computed Tomography Scan ◽  
Model Based ◽  
Tomography Scan

scholarly journals Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

2020 ◽  
Vol 12 (4) ◽  
pp. 739
Author(s):  
Keiller Nogueira ◽  
Gabriel L. S. Machado ◽  
Pedro H. T. Gama ◽  
Caio C. V. da Silva ◽  
Remis Balaniuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Impact ◽  
Automatic Machine ◽  
Feature Representation ◽  
Data Driven ◽  
Maintenance Costs ◽  
Crucial Step ◽  
Machine Learning Methods ◽  
High Resolution Images

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.

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