scispace - formally typeset
Open AccessJournal ArticleDOI

Machine Learning in Medical Imaging

Reads0
Chats0
TLDR
This article will discuss very different ways of using machine learning that may be less familiar, and will demonstrate through examples the role of these concepts in medical imaging.
Abstract
This article will discuss very different ways of using machine learning that may be less familiar, and we will demonstrate through examples the role of these concepts in medical imaging. Although the term machine learning is relatively recent, the ideas of machine learning have been applied to medical imaging for decades, perhaps most notably in the areas of computer-aided diagnosis (CAD) and functional brain mapping. We will not attempt in this brief article to survey the rich literature of this field. Instead our goals will be 1) to acquaint the reader with some modern techniques that are now staples of the machine-learning field and 2) to illustrate how these techniques can be employed in various ways in medical imaging.

read more

Citations
More filters
Book ChapterDOI

How many trees in a random forest

TL;DR: Analysis of whether there is an optimal number of trees within a Random Forest finds an experimental relationship for the AUC gain when doubling the number of Trees in any forest and states there is a threshold beyond which there is no significant gain, unless a huge computational environment is available.
Journal ArticleDOI

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers

TL;DR: It is shown that it is possible to infer unexpected but useful information from ML classifiers and that this kind of information leakage can be exploited by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights.
Journal ArticleDOI

3D Deep Learning on Medical Images: A Review.

TL;DR: The history of how the 3D CNN was developed from its machine learning roots is traced, a brief mathematical description of3D CNN is provided and the preprocessing steps required for medical images before feeding them to 3DCNNs are provided.
Journal ArticleDOI

Deep Learning in Microscopy Image Analysis: A Survey

TL;DR: A snapshot of the fast-growing deep learning field for microscopy image analysis, which explains the architectures and the principles of convolutional neural networks, fully Convolutional networks, recurrent neural Networks, stacked autoencoders, and deep belief networks and their formulations or modelings for specific tasks on various microscopy images.
Journal ArticleDOI

A Review on a Deep Learning Perspective in Brain Cancer Classification

TL;DR: The relationship between brain cancer and other brain disorders like stroke, Alzheimer's, Parkinson's, and Wilson’s disease, leukoriaosis, and other neurological disorders are highlighted in the context of machine learning and the deep learning paradigm.
References
More filters

Statistical learning theory

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI

The Elements of Statistical Learning

Eric R. Ziegel
- 01 Aug 2003 - 
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
Journal ArticleDOI

Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.

TL;DR: Recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity are reviewed.
Journal ArticleDOI

Sparse bayesian learning and the relevance vector machine

TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Journal ArticleDOI

Investigations into resting-state connectivity using independent component analysis

TL;DR: A probabilistic independent component analysis approach, optimized for the analysis of fMRI data, is reviewed and it is demonstrated that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions.
Related Papers (5)