Machine Learning in Medical Imaging.
By Maryellen Lissak Giger. 2018
238 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, 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
- Michael Terry
- Carrie J. Cai
- Lauren Wilcox
- Arvid Lundervold
- Samantha Winter
- Meghavi Rana
- Megha Bhushan
- David F. Steiner
In Biomedical Engineering
- Lauren Wilcox
- Michael Terry
- Arvid Lundervold
- Carrie J. Cai
- Samantha Winter
- Meghavi Rana
- Megha Bhushan
- David F. Steiner
In Radiology, Nuclear Medicine and Imaging
- Meghavi Rana
- Megha Bhushan
- Lauren Wilcox
- Michael Terry
- Arvid Lundervold
- Carrie J. Cai
- Samantha Winter
- David F. Steiner
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, methodology): “In [33], significant features were identified using BI-RADS (Breast Imaging Reporting and Data System) to develop a CAD system for obtaining breast ultrasound.”
- Radiomics: the facts and the challenges of image analysis (2018, background): “Alternatively, machine-learning techniques, underlying the idea that computers may learn from past examples and detect hard-to-discern patterns from large and complex data sets, are emerging as useful tools that may lead to the selection of appropriate features [10 – 12].”
- Deep learning in medical imaging and radiation therapy. (2018, unknown): “285”
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives (2024, background): “Despite these innovations, modern imaging technology continues to rely on high-quality images and skilled practitioners to accurately interpret and identify potential ailments [84].”
- "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, methodology): “For example, one promising application of machine learning (ML) is its use as a “second set of eyes” to inspect a clinical case [6, 26], with the goal of increasing 104:2 Carrie J. Cai et al. the clinician’s accuracy or efficiency.”