I am happy to announce that I have published my final degree project: Deep-Tumour-Spheroid
Deep Learning is making a big impact in areas such as autonomous driving, medicine and robotics among others. In medicine, it is helping doctors to diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments.
This project contributes to an important field like medicine. In particular, the aim of this project is to improve the automatic segmentation of Glioblastoma Multiforme Tumors (GBM) by using Deep Learning. GBMs are the most frequent, aggressive and lethal of all primary brain tumors.
This project presents a comparison of different Deep Learning architectures that could be employed to solve this problem. All of them trained in a similar way by using the PyTorch and FastAI2 libraries. The best model obtained by those architectures can be used through a web application developed specifically for this task.
As a result of this project, the models can segment the tumors in an autonomous way, reducing the work of researchers. Therefore, they can focus on what is important: try different treatments to beat GBM tumours.
I tried the following architectures: UNet, DeepLabV3+, HRNet Seg, Mask RCNN and U²-Net. I used SemTorch for training them easily.
I have another post for speaking about this package
Notebooks can be found here
The best model generalizes pretty good.
I created a Python app that allows doctors to segment tumours easily.
It can be found here
I also created a Web App. It can be found here