Orakl Oncology Uses Meta DINOv2 for Cancer Drug Discovery Acceleration

Title

Orakl Oncology Uses Meta DINOv2 for Cancer Drug Discovery Acceleration

Content

Orakl Oncology, a spinoff from the Gustave Roussy Institute in France, aims to accelerate cancer research and drug development by combining experimental lab-based insights and machine learning. Their mission is to help researchers and developers identify effective therapies for cancer patients during clinical trials, streamlining the discovery process. To achieve this, Orakl Oncology conducts tests on lab-grown cancer cells, known as organoids, to simulate how drugs might perform on actual patients. Thanks to work from academic collaborators from the Jaulin lab and CentraleSupelec, and as part of the RHU ORGANOMIC initiative, the team recognized that they needed quantitative solutions to interpret the vast amounts of imaging data they were generating. Meta DINOv2 stood out as the ideal choice, thanks to its ability to learn from vast image collections and empower high-performance computer vision models. While the researchers previously used models specialized for organoids, they found DINOv2 was more effective. As an open source model, DINOv2 quickly made a major impact by saving the Orakl Oncology team time and increasing their efficiency, as they were able to quickly train DINOv2 on organoid images to more accurately predict patient responses in clinical settings based on lab data. DINOv2 outperformed other models with an accuracy improved by 26.8% compared to other techniques. The availability of DINOv2 has opened up new avenues for research by eliminating some of the more time-consuming aspects of the team work. For example, extracting relevant information from videos previously required labor-intensive analysis of individual frames or sequences. Now, with DINOv2, this data can be directly extracted from videos, allowing researchers to focus on downstream tasks.