- Aiding the doctor in planning the ideal patient care, by supporting the tedious and error-prone cancer classification with Deep Neural Networks.
- Enabling patients all around the world - even in remote places - to receive (better) care through a new decentralized apeer-based ecosystem
What it does
The doctor can upload a tissue sample to Apeer, where it is automatically processed and where a report is generated for the doctor, informing him about the decision in cancer or no_cancer together with a confidence metric.
How we built it
Divided up the work to match compatibility with our team members' skills. The Deep Learning models were trained on the Azure cloud VMs and then a docker image was created with the generated weights, which was then put in the APEER pipeline.
Challenges we ran into
Setting up Jupyter notebooks with azure, building a docker for the apeer pipeline
Accomplishments that we're proud of
Set up a sophisticated pipeline for training out models in the Azure cloud VMs. Used dynamic resources available to train 4 models simultaneously in 4 different GPU simultaneously. Tested the robustness of the Azure services and were quite happy with it.
What we learned
We learned how to learn how to put Deep Learning models in production through the APEER platform. Most importantly learned many new technologies while trying to help out team members and building our product in such a short period of time.
What's next for DOCTAR - Deep Optical Cancer Tissue Apeer Research
Enabling rapid adoption of the ecosystem by pathologists, doctors, and patients through successively developing software services built upon the ideas of transparency and interpretability.