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Diagnostic Image Analysis Group

Diagnostic Image Analysis Group

The Diagnostic Image Analysis Group is part of the Departments of Radiology and Nuclear Medicine, Pathology, and Ophthalmology of Radboud University Medical Center. We develop computer algorithms to aid clinicians in the interpretation of medical images and thereby improve the diagnostic process.

The group has its roots in computer-aided detection of breast cancer in mammograms, and we have expanded to automated detection and diagnosis in breast MRI, ultrasound and tomosynthesis, chest radiographs and chest CT, prostate MRI, neuro-imaging and the analysis of retinal and digital pathology images. The technology we primarily use is deep learning.

It is our goal to have a significant impact on healthcare by bringing our technology to the clinic. We are therefore fully certified to develop, maintain, and distribute software for analysis of medical images in a quality controlled environment (MDD Annex II and ISO 13485).

On this site you find information about the history of the group and our collaborations, an overview of people in DIAG, current projects, publications and theses, contact information, and info for those interested to join our team.

Highlights

November, 2018

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Peter Bandi and Oscar Geessink, organizers of CAMELYON17, challenged participants to move from individual metastases detection (CAMELYON16) to classification of lymph node status on a patient level. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the deadline. The algorithmic details of the top-twelve best submissions are discussed in the paper that was accepted for publication in IEEE Transactions on Medical Imaging last August. Likelihood maps of the top 2 teams are depicted above in high and low magnification. The top row shows a detected contamination, the bottom shows a correct detection of micro metastasis. Read here which architecture and methodology led to the best results and what pushed the highest kappa value from 0.89 to 0.93.

More Research Highlights.

News

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