One of the main treatments for cancer is radiotherapy, which uses x-rays to destroy cancer cells. The good thing with radiotherapy is that it kills the cancer cells for good. The bad thing is that the x-rays may also kill normal cells. Normal cells have a good chance of recovery, but they don’t always recover.
Radiotherapy has other drawbacks, too. It takes time to recover from the treatment. It may harm fertility. And it’s harmful to the patient’s sex life, at the very least. The more optimized the treatment can be, the more doctors can help patients with fewer negative effects.
Deep learning can be applied to tumor detection via segmentation masks. These are masks used to separate bad cells from good ones in an image, pixel by pixel. To be most effective, the ideal mask should be fitted as closely to just the bad cells as possible. Creating such an accurate, detailed mask requires a lot of expertise, for which training is difficult. It’s also a very time-consuming task for humans.
Deep learning, on the other hand, is brilliant at analyzing images and can be applied to the creation of segmentation masks for any type of tumor. In a project testing this theory on brain tumor segmentation masks, data scientists at Peltarion worked together with a leading radiotherapy company to create deep learning models on the Peltarion Platform.