The use of the medical ontology for a semantic-based fusion system in biomedical informatics Application to Alzheimer disease

Abstract

The Unified Medical Language System (UMLS) 1 offers the possibility to use annotated medical terms for Computer Aided Diagnoses System (CADS). We present a new semantic fusion system, based on UMLS. This fusion system has applications on a CADS that diagnoses neurodegenerative diseases. Since the UMLS Metathesaurus contains a huge amount of data, classification and extraction of the data we use is necessary. For this purpose, we use a feedforward neural network which is capable of training the negative patterns as well as the positive ones. At the semantic level we generate a three-layered network structure, which gives us the possibility of adding medical knowledge in order to cluster the data and prepare it for the fusion process.

Medical image fusion: A survey of the state of the art

“…The use of secondary data to estimate instantaneous model parameters of diabetic heart disease is explained in [323]. A semantic based fusion technique in application to Alzheimer's disease is presented in [324]. A fusion imaging using a hybrid SPECT-CT camera is used in colorectal [250] cancer patients.…”

Comparative assessment of text-image fusion models for medical diagnostics

“…For example, in [29,30], the characteristics of intensity, hue and saturation are used for this. The selected features can be semantically labelled using anatomical brain atlases [31], special visual indexes [31] or ontologies [32]. The ontology describes the medical terms via a controlled vocabulary, where the conceptualizations of the domain knowledge are constructed as an OWL (Ontology Web Language) model.…”

“…As the analysis of literature sources shows and as confirmed by the practice of radiologists [41], it is challenging to establish a formal correspondence between any fragments of medical images and fragments of medical reports describing them without involving semantic interpretation in both domains. Attempts to use external structures for this (such as visual indexes [31] or ontologies [32]) lead to significant losses in context, which in many cases decreases the benefits of multimodal fusion. Therefore, in recent publications, approaches related to the use of features that preserve contextual domain dependencies dominate [18,19,[21][22][23], and УПРАВЛЕНИЕ В МЕДИЦИНЕ И БИОЛОГИИ the deep learning methods are used as a technological base.…”