A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Unifying the error-correcting and output-code AdaBoost within the margin framework
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multiclass boosting with repartitioning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A reliable method for cell phenotype image classification
Artificial Intelligence in Medicine
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Serial fusion of random subspace ensemble for subcellular phenotype images classification
International Journal of Bioinformatics Research and Applications
| Hi-index | 12.05 |
Automated cell phenotype image classification is related to the problem of determining locations of protein expression within living cells. Localization of proteins in cells is directly related to their functions and it is crucial for several applications ranging from early diagnosis of a disease to monitoring of therapeutic effectiveness of drugs. Recent advances in imaging instruments and biological reagents have allowed fluorescence microscopy to be extensively used as a tool to understand biology at the cellular level by means of the visualization of biological activity within cells. However, human classification of fluorescence cell micrographs is still subjective and very time consuming, thus an automated approach for the systematic determination of protein subcellular locations from fluorescence microscopy images is required. Existing approaches concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. This paper takes into consideration the best methods proposed in the literature and focuses on the study of ensemble machine learning techniques for cell phenotype image classification. Two techniques are tested for the classification: a random subspace of Levenberg-Marquardt neural networks and a variant of the AdaBoost. Each of these two methods are tested with different feature sets, moreover the fusion between the two ensembles is studied. The best ensemble tested in this work obtains an outstanding 97.5% accuracy in the 2D-Hela dataset, which to the best of our knowledge is the best performance obtained on this dataset (the most used benchmark for comparing automated cell phenotype image classification approaches).