Ver el código de «KES-2012-Neurocomputing-issue-scope»

;Title of the special issue: Bioinspired knowledge based techniques in medical image and information processing

;Acronym: KES medical image and information
 
;Definition of issue’s scope: 
 
This special issue identifies the following knowledge related challenges in the current  field of medical image and information processing: 
:- Combining high (semantic) and low level information for pattern recognition.  Such needs appear in apparently unrelated fields like motion understanding, content based image retrieval (CBIR) 
:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator. 
which can not be subject of conventional  error measures, impeding the application of state-of-the-art error minimization approaches. The sources of information are heterogeneous, and its combination leads to unmeasurable data types. In the field of biomedical information processing, 
:- New views on uncertainty, ambiguity and data artifacts (i.e. noise, missing data) allowing to effectively deal with them in a systematic and integrative way. 


These challenges are currently being addressed from a number of points of view. The 
development of semantic based pattern recognition systems, able to learn ontologies 
from data, even heterogeneous data, as well as of ontology based reasoning allow for 
the interplay between high-level semantics and low-level features. Hybrid systems 
combining ontologies with fuzzy systems allow introduce uncertain modeling and 
reasoning in the semantic domain. Lattice computing approaches allow seamlessly 
treatment of heterogeneous data through lattice theory, while allowing new more robust 
reasoning process diverging from the conventional statistics framework. Still, despite a 
long tradition of research and results, new classification algorithms are being extended 
to deal with heterogenous, ambiguous and artifact prone data. Also new ways of 
interactive development of systems, such as active learning or reinforcement feedback 
approaches can help to improve the efficiency of learning under scarce or expensive 
data collection. The special issue invites works on these areas showing the interplay 
between high level semantics and low level statistical and bio-inspired data processing 
techniques.  
 
;Specific technical topics 
:- Bio-inspired machine learning: artificial neural networks, evolutionary computation
:- Fuzzy and probabilistic machine learning approaches 
:- Lattice computing approaches  
:- Active learning, reinforcement learning 
:- Advanced classification systems (ensembles, ELM) 
:- Interplay of high level semantics and low level machine learning 
:- Sparse bayesian machine learning approaches
:- Atlas and other a priori information
;Applications 
:- Remote sensing 
:- Multimedia information processing 
:- Biometrics, such as face recognition 
:- Fusion of clinical and medical image information  
:- Content based image retrival 
:- Multimodal information processing 
 


The specific issue's topic will be the application of bio-inspired
machine learning for the processing of medical information, including
clinical, biochemical and medical image information, with an emphasis in
two venues: (a) circulatory system diseases, i.e. vessel image
processing, and related clinical information, and (b) neurodegenerative
diseases, including Alzheimer's Disease and neuropsychyatric disorders,
involving multimodal brain imaging and related clinical information. The
kind of systems would range from image segmentation, registration and
normalization to computed aided diagnosis systems, brain state decoding
and prediction. Please let me know your views.


Submission deadline: 1st December 2012