see Biopsychosocial clustering
The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
Given a set of data points, we can use a cluster algorithm to classify each data point into a specific group.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
: These algorithms are used to group similar data points together based on their intrinsic properties. Common clustering algorithms include:
K-Means Clustering Hierarchical Clustering DBSCAN Gaussian Mixture Models (GMM) Dimensionality Reduction: These algorithms reduce the number of features in a dataset while retaining important information. Common dimensionality reduction techniques are:
Principal Component Analysis (PCA) t-Distributed Stochastic Neighbor Embedding (t-SNE) Linear Discriminant Analysis (LDA) Anomaly Detection: Anomaly detection algorithms identify unusual data points that do not conform to expected patterns. Algorithms in this category include:
Isolation Forest One-Class SVM Local Outlier Factor (LOF)
Reliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon's visual aspect and tactile perception of the considered tissue, which is-due to inherent low brain consistency contrast-a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when-as for neuroendoscopy-tactile perception will be damped or-as for surgical robotic applications-will not be a priori existent.
Stroop et al. from the Department of Neurosurgery, Academic Hospital Cologne-Merheim, Department of Engineering Technology (INDI), Vrije Universiteit Brussels, Belgium, presented the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.
A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E1 and E2 for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate cluster algorithm.
Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.
Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising 1).