Machine Learning and Interpretation in Neuroimaging - Info and Reading Options
By Georg Langs
“Machine Learning and Interpretation in Neuroimaging” Metadata:
- Title: ➤ Machine Learning and Interpretation in Neuroimaging
- Author: Georg Langs
“Machine Learning and Interpretation in Neuroimaging” Subjects and Themes:
- Subjects: ➤ Machine learning - Congresses - Artificial intelligence - Medical applications - Brain - Imaging - Data processing - Diagnostic imaging - Brain, imaging - Pattern perception - Computer science - Data mining - Computer vision - Optical pattern recognition - Computer Imaging, Vision, Pattern Recognition and Graphics - Data Mining and Knowledge Discovery - Probability and Statistics in Computer Science - Image Processing and Computer Vision - Computer Applications
Edition Identifiers:
- The Open Library ID: OL20769595W
AI-generated Review of “Machine Learning and Interpretation in Neuroimaging”:
"Machine Learning and Interpretation in Neuroimaging" Description:
The Open Library:
Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
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