Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

Burgeoning career opportunities in radiation oncology.

Big Data in Radiation Oncology: Challenges and Opportunities

to its full capacity without further delay. Firstly, the current feefor-service healthcare model in United States is not going to be sustainable. The United States spends about $640 billion or 17% of its Gross Domestic Product (GDP) on health care per year, much more than any other major developed countries such as the United Kingdom, Germany and Denmark, with outcomes no better than those of i...

Machine Learning and Citizen Science: Opportunities and Challenges of Human-Computer Interaction

Background and Aim: In processing large data, scientists have to perform the tedious task of analyzing hefty bulk of data. Machine learning techniques are a potential solution to this problem. In citizen science, human and artificial intelligence may be unified to facilitate this effort. Considering the ambiguities in machine performance and management of user-generated data, this paper aims to...

Challenges and Opportunities in Applied Machine Learning

and generating land-cover maps of the Earth have in common? Both are real-world problems for which we have applied machine-learning techniques to assist human experts, and in each case doing so has motivated the development of novel machine-learning methods. Our research group works closely with domain experts from other disciplines to solve practical problems. For many tasks, off-the-shelf met...

Requirements for Machine Lifelong Learning

A significant advance in inductive modelling are systems that retain learned knowledge and selectively transfer portions of that knowledge as a source of inductive bias. We define such to be machine lifelong learning (ML3) systems. This paper makes an initial effort at specifying the scope of ML3 systems and their functional requirements.