Latest and Exciting Innovations in all the Areas of Artificial Intelligence
Machine Learning and Computational Intelligence
There is an increasing need for the application of Machine Learning (ML) and Computational Intelligence (CI) techniques, which can effectively perform image processing operations (such as segmentation, co-registration, classification, and dimensionality reduction), in the fields of neuroimaging and oncological imaging. Although the manual approach often remains the golden standard in some tasks (e.g., segmentation), ML can be exploited to automate and facilitate the work of researchers and clinicians. Frequently used techniques include Support Vector Machines (SVMs) for classification problems, graph-based methods, and Artificial Neural Networks (ANNs).
More recently, deep ANNs have shown to be very successful in computer vision tasks owing to the ability to automatically extract hierarchical descriptive features from input images. It has also been used in the oncological and neuroimaging domains for automatic disease diagnosis, tissue segmentation, and even synthetic image generation. The main issue, however, remains the relative sample paucity of the typical imaging datasets that leads to a poor generalisation of the employed deep ANNs, considering the high number of required parameters. Consequently, parameter-efficient design paradigms, specifically tailored to medical applications, ought to be devised, also by exploiting CI-based techniques (e.g., neuroevolution).
In this context, these advanced ML techniques can be suitably exploited to combine heterogeneous sources of information, allowing for multi-omics data integration. Such a kind of analyses may represent a significant step towards personalised medicine.
Topics of interest include but are not limited to:
- ML techniques for segmentation, co-registration, classification, or dimensionality reduction of medical images
- Deep neural networks for medical image super-resolution, de-noising and synthesis
- Deep Learning for neuroimaging and oncological imaging analysis
- Integration of multi-comics’ data
- Brain network analysis
- Application of graph theory to MRI and functional MRI (fMRI) data
- Application of ML methods for neurodegenerative disease studies
- Computational modelling and analysis of neuroimaging
- Methods of analysis for structural or functional connectivity
- Development of new neuroimaging tools
- Radiomic analyses for tumour phenotypes
- Radio genomics for intra- and inter-tumoural heterogeneity evaluation
- Generative adversarial models for data augmentation and image super-resolution
- CI methods for optimizing medical image analysis tasks
The journal invites different types of articles including original research article, review articles, short note communications, case reports, Editorials, letters to the Editors and expert opinions & commentaries from different regions for publication.
A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/eye-diseases-and-disorders.html
The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.
International journal of swarm intelligence and evolutionary computation