The use of artificial neural networks in decision support in cancer: A systematic review

By Paulo J. G. Lisboa, Azzam Fouad George Taktak. 2006

483 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gary S. Collins
2. B. Van calster
3. Evangelia Christodoulou
4. J. Verbakel
5. Ewout W. Steyerberg
6. Jinchang Ren
7. Georgia D. Tourassi
8. Maciej A. Mazurowski

In Radiology, Nuclear Medicine and Imaging

1. Maciej A. Mazurowski
2. Gary S. Collins
3. Soib Taib
4. Mohd Shawal Bin Jadin

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, neutral): “Still, in 2006 Lisboa and Taktak [266] examined the use of artificial neural networks in medical journals, concluding that they improved healthcare relative to traditional screening methods in 21 of 27 studies.”
• Recent Progress in Diagnosing the Reliability of Electrical Equipment by Using Infrared Thermography (2012, methodology): “Currently, automatic inspections of IRT images are widely applied in medical imaging [25–32], nondestructive testing [33,34], defect detection in structures [35,36] and so on.”
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Although ML methods have been sporadically used for clinical prediction for some time [3,4], the growing availability of increasingly large, voluminous, and rich data sets such as electronic health records data have reignited interest in exploiting these methods [5e7].”
• Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance (2008, background): “In particular, neural network classifiers are a very popular choice for medical decision making and they have been shown to be very effective in the clinical domain (Lisboa, 2002; Lisboa & Taktak, 2006).”
• Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, background): “Although the techniques of AI differ from diagnosis to prognosis, both applications still need validation, and this is challenging due to the large amount of data needed to achieve robust results [73].”
• Medical image analysis with artificial neural networks (2010, background): “Some relevant survey papers can be found in [59, 60, 61].”