The road to commencement is a long one, especially for graduate students whose degree programs may take upwards of six years. There are many moments when focus may be lost and excitement may dwindle. Faculty mentors can play a key role in helping students persevere.

MIT researchers have devised a method that accelerates the process for creating and customizing templates used in medical-image analysis, to guide disease diagnosis.  

One use of medical image analysis is to crunch datasets of patients’ medical images and capture structural relationships that may indicate the progression of diseases. In many cases, analysis requires use of a common image template, called an “atlas,” that’s an average representation of a given patient population. Atlases serve as a reference for comparison, for example to identify clinically significant changes in brain structures over time.

In opening the AI and the Work of the Future Congress, MIT Professor Daniela Rus presented diverging views of how artificial intelligence will impact jobs worldwide.

By automating certain menial tasks, experts think AI is poised to improve human quality of life, boost profits, and create jobs, said Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science.

“For me, to be really fulfilled in my work as a scientist, I want to have some tangible impact,” she says. 

Carpenter explains that artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.

MIT researchers have developed a model that recovers valuable data lost from images and video that have been “collapsed” into lower dimensions.

The model could be used to recreate video from motion-blurred images, or from new types of cameras that capture a person’s movement around corners but only as vague one-dimensional lines. While more testing is needed, the researchers think this approach could someday could be used to convert 2D medical images into more informative — but more expensive — 3D body scans, which could benefit medical imaging in poorer nations.

Assessing placental health is difficult because of the limited information that can be gleaned from imaging. Traditional ultrasounds are cheap, portable, and easy to perform, but they can’t always capture enough detail. This has spurred researchers to explore the potential of magnetic resonance imaging (MRI). Even with MRIs, though, the curved surface of the uterus makes images difficult to interpret.

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient’s risk of cardiovascular death. 

Josh Tenenbaum, a professor in MIT’s Department of Brain and Cognitive Sciences who studies human cognition, has been named a recipient of a 2019 MacArthur Fellowship.