Deep Learning for Cancer Research: APEER at Hackathon

Image Processing

APEER offers opportunity to train neural networks for cancer research at the Scion Hackathon in Karlsruhe

Team APEER will be part of Scion, a three-day conference and hackathon focused on machine learning in medicine. Participants receive the rare chance to learn how deep learning approaches help in today’s cancer research. The hackathon will take place from October 26th until October 28th in the Technology Centre in Karlsruhe. Students from the fields of computer science, design, biology and medicine will form small teams and work on image analysis challenges.

The APEER challenge contribution to the hackathon revolves around Bio-Electron Microscopy in cancer research:

Interaction between the organs of cells, such called cell organelles, play an increasing role in terms of cancer research. Two of the most important cell organelles are the mitochondria (energy generation) and the endoplasmatic reticulum (protein synthesis). Their examination is subject of many working groups of the biomedical basic research. On the basis of our ground truth data that come from a high resolution electron microscope, you develop a rough model for the above mentioned detection of the cellular substructures. As former approaches with thresholds are not useful in this context, we are going to apply the Deep Learning approach here.

Roman Zinner, partner manager at APEER, is proud to offer ground truth data to the participants: “This will be a real world challenge for students and young professionals to use deep learning as a tool to address applications in the megatrend of scientific cancer research.” Registration to the hackathon is open. The hackathon is accompanied by keynote speeches from doctors, researchers and industry representatives. The manufacturer of research microscopes ZEISS will be presenting a microscope to familiarize students with the entire research process: from image acquisition to processing to analysis.


The digital microscopy platform APEER released its beta version in July 2018. It enables researchers to create and customize their data analysis and image processing tasks and was designed to offer matchmaking between computer scientists or researchers with coding skills and those who need scientific software solutions. APEER was initated by ZEISS.

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Copyright of header image: Sample courtesy of S. Gawrzak and M. Jechlinger, EMBL, Heidelberg, Germany

Thomas Irmer

Software Developer

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