KES-2012-Neurocomputing-issue-scope - Historial de revisiones

Manuel.grana en 21:23 29 mar 2013

2013-03-29T21:23:24Z

← Revisión anterior		Revisión del 23:23 29 mar 2013
Línea 32:		Línea 32:
	:- image segmentation, registration and normalization		:- image segmentation, registration and normalization
	:- brain state decoding and prediction		:- brain state decoding and prediction



	~~Submission deadline: December 2012- January 2013~~

Manuel.grana en 22:19 22 oct 2012

2012-10-22T22:19:10Z

← Revisión anterior		Revisión del 00:19 23 oct 2012
Línea 24:		Línea 24:
	:- Sparse bayesian machine learning approaches		:- Sparse bayesian machine learning approaches
	:- Atlas and other a priori information		:- Atlas and other a priori information
			:- Mixing qualitative and quantitative data sources
	;Domains of application		;Domains of application
	:- circulatory system diseases, i.e. vessel image processing, and related clinical information		:- circulatory system diseases, i.e. vessel image processing, and related clinical information

Manuel.grana en 22:18 22 oct 2012

2012-10-22T22:18:08Z

← Revisión anterior		Revisión del 00:18 23 oct 2012
Línea 12:		Línea 12:


	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.~~		These challenges are currently being addressed from a number of points of view. The development of semantics 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 and bio-inspired 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 bio-inspired and statistical 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.


	;Specific technical topics		;Specific technical topics

Manuel.grana en 22:11 22 oct 2012

2012-10-22T22:11:44Z

← Revisión anterior		Revisión del 00:11 23 oct 2012
Línea 33:		Línea 33:


	Submission deadline: ~~1st~~ December 2012		Submission deadline: December 2012- January 2013

Manuel.grana en 22:11 22 oct 2012

2012-10-22T22:11:17Z

← Revisión anterior		Revisión del 00:11 23 oct 2012
Línea 12:		Línea 12:


	These challenges are currently being addressed from a number of points of view. The		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.
	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		;Specific technical topics

Manuel.grana en 22:02 22 oct 2012

2012-10-22T22:02:08Z

← Revisión anterior		Revisión del 00:02 23 oct 2012
Línea 5:		Línea 5:
	;Definition of issue’s scope:		;Definition of issue’s scope:

	This special issue identifies the following ~~knowledge related~~ challenges in the ~~current~~ field of medical image and information processing:		This special issue identifies the following current challenges in the field of medical image and information processing:
	:- Combining high (semantic) and low level information for clinical decision support (CDS) or computer aided diagnosis (CAD) systems		:- Combining high (semantic) and low level information for clinical decision support (CDS) or computer aided diagnosis (CAD) systems
	:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator: including		:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator: including
	clinical, biochemical and medical image information		clinical, biochemical and medical image 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. 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.		:- 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.

Línea 38:		Línea 37:
	:- Sparse bayesian machine learning approaches		:- Sparse bayesian machine learning approaches
	:- Atlas and other a priori information		:- Atlas and other a priori information
	;~~Applications~~		;Domains of application
	:- ~~Remote sensing~~		:- circulatory system diseases, i.e. vessel image processing, and related clinical information
	~~:- Multimedia~~ information ~~processing~~		:- neurodegenerative diseases, including Alzheimer's Disease
	:- ~~Biometrics~~, ~~such as face recognition~~		:- genetics and proteomics
	:- ~~Fusion of clinical~~ and ~~medical image information~~		:- brain plasticity, i.e. recovery from ictus
	:- ~~Content based image retrival~~		:- image segmentation, registration and normalization
	:- ~~Multimodal information processing~~		:- brain state decoding and prediction



	~~The specific issue's topic will be the application of bio~~-~~inspired~~
	~~machine learning for the processing of medical 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		Submission deadline: 1st December 2012

Manuel.grana en 21:55 22 oct 2012

2012-10-22T21:55:50Z

Manuel.grana en 21:53 22 oct 2012

2012-10-22T21:53:36Z

← Revisión anterior		Revisión del 23:53 22 oct 2012
Línea 6:		Línea 6:

	This special issue identifies the following knowledge related challenges in the current field of medical image and information processing:		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.		:- 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)
	Such needs appear in apparently unrelated fields like motion understanding,		:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.
	content based image retrieval (CBIR)		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,
	:- Combining multi-modal information which can not be subject of conventional		:- 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.
	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.


Línea 52:		Línea 45:
	:- Multimodal information processing		:- 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		The specific issue's topic will be the application of bio-inspired
	machine learning for the processing of medical information, including		machine learning for the processing of medical information, including

Manuel.grana en 21:50 22 oct 2012

2012-10-22T21:50:12Z

← Revisión anterior		Revisión del 23:50 22 oct 2012
Línea 1:		Línea 1:
	;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

Manuel.grana en 21:20 22 oct 2012

2012-10-22T21:20:00Z

← Revisión anterior		Revisión del 23:20 22 oct 2012
Línea 52:		Línea 52:
	:- Multimodal information processing		:- 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		Submission deadline: 1st December 2012

← Revisión anterior		Revisión del 23:55 22 oct 2012
Línea 6:		Línea 6:

	This special issue identifies the following knowledge related challenges in the current field of medical image and information processing:		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 high (semantic) and low level information for clinical decision support (CDS) or computer aided diagnosis (CAD) systems
	:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator.		:- Combining multi-modal information. A wide diversity information sources may be used to reach a diagnosis that can be validated by the human operator: including
			clinical, biochemical and medical image 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. In the field of biomedical information processing,		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.		:- 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.
Línea 48:		Línea 49:

	The specific issue's topic will be the application of bio-inspired		The specific issue's topic will be the application of bio-inspired
	machine learning for the processing of medical information, ~~including~~		machine learning for the processing of medical information,
	~~clinical, biochemical and medical image information~~, with an emphasis in
			, with an emphasis in
	two venues: (a) circulatory system diseases, i.e. vessel image		two venues: (a) circulatory system diseases, i.e. vessel image
	processing, and related clinical information, and (b) neurodegenerative		processing, and related clinical information, and (b) neurodegenerative