StainGAN^?: Stain style transfer for digital histological images

Supervision: Prof. Dr. Nassir Navab, Shadi Albarqouni, Christoph Baur

Abstract

Digitized Histopathological diagnosis is in increasing demand, but stain color variations due to stain preparation, differences in raw materials, manufacturing techniques of stain vendors and use of different scanner manufacturers are imposing obstacles to the diagnosis process. The problem of stain variations is a well-defined problem with many proposed methods to overcome it each depending on the reference slide image to be chosen by a pathologist expert. We propose a deep-learning solution to that problem based on the Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks eliminating the need for an expert to pick a representative reference image. Our approach showed promising results that we compare quantitatively and quantitatively against the state of the art methods.

Tasks:

• Understand GANs
• Transfer the knowledge to Medical Image Synthesis
• Validate the proposed algorithm on clinical data

Requirements:

• Good understanding of medical physics
• Good understanding of statistics and machine learning methods.
• Very good programming skills in Python & TensorFlow^? / pyTorch

Location:

• Garching

Resultant Paper

M. T. Shaban, C. Baur, N. Navab, S. Albarqouni StainGAN: Stain Style Transfer for Digital Histological Images Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy A pre-print version is available online at arXiv. (bib)