Diferencia entre revisiones de «KES-2012-Neurocomputing-issue-scope»

De Grupo de Inteligencia Computacional (GIC)
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Sin resumen de edición
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;Title of the special issue: Bioinspired knowledge based techniques in medical image processing
;Title of the special issue: Bioinspired knowledge based techniques in medical image and information processing


;Acronym: KES medical image
;Acronym: KES medical image and information
   
   
;Definition of issue’s scope:  
;Definition of issue’s scope:  
   
   
This special issue identifies the following knowledge related challenges in the current  
This special issue identifies the following knowledge related challenges in the current field of medical image and information processing:  
field of pattern recognition:  
:- Combining high (semantic) and low level information for pattern recognition.  
:- Combining high (semantic) and low level information for pattern recognition.  
Such needs appear in apparently unrelated fields like motion understanding,  
Such needs appear in apparently unrelated fields like motion understanding,  
Línea 37: Línea 36:
   
   
;Specific technical topics  
;Specific technical topics  
:- Ontology reasoning and learning  
:- Bio-inspired machine learning: artificial neural networks, evolutionary computation
:- Fuzzy and probabilistic ontologies
:- Fuzzy and probabilistic machine learning approaches
:- Lattice computing approaches   
:- Lattice computing approaches   
:- Knowledge based adaptive approaches 
:- Active learning, reinforcement learning  
:- Active learning, reinforcement learning in qualitative spaces
:- Advanced classification systems (ensembles, ELM)  
:- Advanced classification systems (ensembles, ELM, clustering)  
:- Interplay of high level semantics and low level machine learning  
:- Interplay of high level semantics and low level machine learning  
:- Sparse bayesian machine learning approaches
:- Atlas and other a priori information
;Applications  
;Applications  
:- Remote sensing  
:- Remote sensing  

Revisión del 23:50 22 oct 2012

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 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. For instance, in the field of biomedical information processing, a wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.

- 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

Dear Tom, thanks for your kind response, I am thinking about 10 papers. 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