(3.1) where UΣV T is the singular value decomposition of X. This is equivalent to transformation into a new coordinate system such that the greatest variance by any projection of…

Fig. 6.1 Visual representation of the sample ontology Table 6.1 RDF representation of a patient based on the ontology of Fig. 6.1 Subject Predicate Object mySet:patient1001 rdf:type ncit:C16960 mySet:patient1001 myOntology:hasFirstName…

Fig. 11.1 An example of regular breathing Fig. 11.2 An example of highly irregular breathing Fig. 11.3 A sequence of measurements of tumor position and chest marker position, showing a…

(8.1) where f(u, v) is a function determined using a machine learning approach, which we choose to be support vector machine (SVM) learning [26], and ζ is the modeling error. The…

Fig. 7.1 Flowchart for a generic CADe scheme for the detection of lesions in diagnostic images. Boxes with solid lines indicate four core steps in the CADe scheme, and those…

Fig. 17.1 As the dose delivered to a tumour increases, so does the probability of tumour control (TCP). However, the resultant increase in dose to surrounding healthy tissues increases the…

Fig. 4.1 The main steps of evaluation Comparison of a new algorithm to other (may be generic or application-specific) classifiers on a specific domain (e.g., when proposing a novel learning…

(14.1) where C j is the j -th cluster, μ j is the center of the j -th cluster, and D stands for the distance between the two points. Each…

(18.1) where d is the fraction size, D is the total delivered dose, t is the difference between the total treatment time (T) and the lag period before accelerated clonogen…

Fig. 2.1 Computational modeling vs. statistical analysis (Adapted from [5]) Machine learning is a branch of computational modeling that inherited many of its properties and utilizes statistical modeling techniques as…