Machine learning is well recognised as an effective tool for researchers to handle problems in the medical and biological fields. It is a method of data analysis that automates analytical model building, using algorithms that iteratively learn from data and then make sophisticated decisions based on the learned practices.
Medicine involves large amounts of important data and medical application problems frequently make human-generated, rule-based heuristics intractable. In this special issue, we aim to provide a forum for presenting cutting-edge machine learning methods for medical and biological applications. Medical and biological applications may include the learning of similarities across different biomechanics; bio-photonics; medical device development, evaluation and commercialization; organ localization; and learning of anatomical changes.
We invite researchers to contribute original research articles and review articles that will address medical diagnosis approaches to provide solutions to existing medical diagnosis and treatment and biological issues.
Suitable topics include, but are not limited to, the following:
- Nano medicine
- Biomedical signals
- Biosensing
- Medical robots
- Novel surgical devices and sensors
- Surgical and interventional systems
- Computational physiology
- Clinical and biological applications
- Imaging and analysis methods for image-guided therapies
- Physician-computer interfaces using virtual/mixed/augmented reality
- Computer-aided modelling and evaluation of surgical procedures
- Biological image computing
- Neuroscience image computing
- Computational anatomy
- Imaging genetics
- Medical image computing
- Visualisation and interaction
Important Dates
Submission of manuscripts: 25 August, 2017
Notification to authors: 25 October, 2017
Final versions due: 25 December, 2017
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