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Computational oncology by analysis and alignment of histopathology slide images

Neta Becker and Shelly Francis

Supervised by Roy Velich, Tal Neoran and Gil Shamai


In modern medicine, biopsy is a common method for diagnosing breast cancer. The tissue sample is sliced into ultra-thin sections, each of which is stained using techniques such as IHC or H&E to highlight different features. A pathologist then examines the stained sections under a microscope to determine if cancerous cells are present.
While IHC staining provides valuable information, it is expensive and time-consuming. H&E staining, on the other hand, is quicker and more cost-effective. Our goal was to reduce the reliance on IHC staining by calculating the alignment between IHC and H&E stained slides. This would allow us to train neural networks using both types of slides, transfer pathological information from one slide to another, and potentially rely solely on H&E stained slides.
To achieve this goal, we developed a GUI that allows matching points between pairs of slides. After obtaining a set of matched points for each pair, we explored various methods for calculating the alignment, including automatic alignment, manual alignment, and triangulation. We examined different models based on various articles and proposed methods for comparing the results achieved through each method in order to develop an algorithm for our goal

Project Computational oncology by analysis and alignment of histopathology slide images Picture 1
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

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