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 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

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.

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,

, 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