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Improving cancer and disease treatments by understanding electromagnetic communication among biological cells

Prof. Kamal Sarabandi and ECE PhD student Navid Barani won a best paper award for their research on how biological cells may use electromagnetic signal transmission to communicate.

New machine learning method improves testing of stem-like tumor cells for breast cancer research

To improve the prediction and identification of stem-like cancer cells, Prof. Euisik Yoon’s group developed a method that is 3.5 times faster than the standard approach.

Enabling large-scale testing of cancer drugs with machine learning

Prof. Euisik Yoon and his team developed a new machine learning tool that enables large-scale testing of cancer drug effectiveness with microfluidics.

Two ‘U’ researchers receive Distinguished University Innovator Award

The Michigan Daily profiles Professors David Blaauw and Dennis Sylvester, who are this year’s recipients of the 2019 Distinguished University Innovator Award.

By Cannibalizing Nearby Stromal Stem Cells, Some Breast Cancer Cells Gain Invasion Advantage

Cancer biologists and engineers collaborated on a device that could help predict the likelihood of breast cancer metastasis.

Laura Balzano aims to improve precision medicine as a Fulbright Scholar

Balzano will work with Portuguese researcher Mário Figueiredo to develop new machine learning methods impacting medical diagnosis and treatment.

Blood biopsy: New technique enables detailed genetic analysis of cancer cells

Capturing cancer cells from blood samples offers a non-invasive way to observe whether the cancer is disappearing or whether it is becoming resistant to the treatment.

Biopsy alternative: “Wearable” device captures cancer cells from blood

New device caught more than three times as many cancer cells as conventional blood draw samples.

An even smaller world’s smallest ‘computer’

The latest from IBM and now the University of Michigan is redefining what counts as a computer at the microscale.

Students win prizes for improving image processing techniques for liver cancer detection and much more

Students in EECS 556: Image Processing, explore methods to improve image processing in applications such as biomedical imaging and video and image compression

‘Sister cell’ profiling aims to shut down cancer metastasis

Michigan engineers release individual cells from a specially-designed chip using laser pulses.

Cancer stem cells: new method analyzes 10,000 cells at once

A new tool for making sense of the cells believed to cause cancer relapses and metastases.

Using data science to achieve ultra-low dose CT image reconstruction

Ultra-low dose CT scans that provide superior image quality could not only benefit patients, but they could open up entirely new clinical applications.

What makes cancer cells spread? New device offers clues

Why do some cancer cells break away from a tumor and travel to distant parts of the body? A team of oncologists and engineers from the University of Michigan teamed up to help understand this crucial question.

Fighting lung cancer: Faster image processing for low-radiation CT scans

This advance could be important for fighting lung cancers, as symptoms often appear too late for effective treatment.

Student Spotlight: Mai Le – Finding a better way to diagnose breast cancer with MRI

The research group is using statistical signal processing to create crisper images with only 20% of the data required by a traditional MRI scan.

Biochips for better cancer therapy

One promising area of cancer treatment is photodynamic therapy, which combines the agents of a photosensitive drug, light, and oxygen.