An open platform for interpretable
analyses of histological samples
We are working to improve diagnostic pathology and people’s lives
Cancer is responsible for 10 million deaths each year. Furthermore, by 2040, cancer incidence is expected to grow by more than 60%.
The first step in successful cancer treatment is a correct pathological diagnosis, which determines the precise type and the severity of the cancer.
Despite technological progress, pathologists continue to examine histological samples manually, which is extremely demanding, time-consuming, costly, and does not allow for consistent reproducibility. Due to increased workloads, misdiagnosis remains high, and by 2030 there will be a deficit of 5700 pathologists in the USA alone.
With our solution, we automate and digitalize the process of identifying differences between healthy and cancerous samples by applying a deep learning algorithm.
The benefits of our solution are:
1. Time – the automated nature of HistoGenie offers a shorter turnaround time compared to traditional microscopy, thus increasing the pathologist’s productivity.
2. Diagnostic accuracy – HistoGenie assesses the differences between histological samples, which leads to improved recognition rates and a better understanding of correlations between carcinogenicity and the morphological properties of cells and tissues.
We are a young multidisciplinary team that covers all the relevant fields of knowledge important for the development of our solution.
The team has important experience in machine learning, cancer biology, oncology, pathology, and business.
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