A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
Artificial convolution neural network for medical image pattern recognition
Neural Networks - Special issue: automatic target recognition
An Operator Which Locates Edges in Digitized Pictures
Journal of the ACM (JACM)
A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Digital Image Processing Algorithms and Applications
Digital Image Processing Algorithms and Applications
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neocognitron capable of incremental learning
Neural Networks
Computers in Biology and Medicine
Backpropagation applied to handwritten zip code recognition
Neural Computation
Fast connected-component labeling
Pattern Recognition
Efficient approximation of neural filters for removing quantumnoise from images
IEEE Transactions on Signal Processing
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A Run-Based Two-Scan Labeling Algorithm
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Evaluation of convolutional neural networks for visual recognition
IEEE Transactions on Neural Networks
Artificial Intelligence in Medicine
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Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computeraided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.