SemanticMD Cloud Compute
Deep learning APIs and web-based classifier management
Train and update deep learning models with ease.
How much time do you spend wrangling data, managing file I/O, and rewriting the same sampling and image pre-processing code? We've made it easy to upload data and instantly train a state-of-the-art network (e.g. ResNet (2016), GoogLeNet (2015), etc.) without writing a single line of code. This greatly speeds up the process and allows you to iterate quickly with different types of deep learning networks including custom models you design.
Custom image processing
for every modality.
How you segment, denoise, and augment medical imaging data can make a big difference regardless of your choice of deep learning network. We've taken out a lot of the guesswork by including modality-specific pre-processing steps so you can focus on building the best deep learning model.
Click below to see an example pipeline.
Train object detectors - in 24 hours or less
If you have ever tried to use code for CRF recognition, parts-based models, or the latest deep learning networks for semantic segmentation, you would have experienced the frustration of broken libraries and Matlab dependencies. We have developed the world's first service to train your own deep learning model for object detection.
We can design custom algorithms for you.
3. We provide a web dashboard to help you monitor the progress and test the models
1. You tell us what needs to be analyzed
2. We design custom deep learning models to fit your data
We created SemanticMD Cloud Compute after several years of working with biologists and machine learning scientists at Carnegie Mellon University. We saw a need in the medical imaging community to have advanced data analysis tools suitable for both scientists without coding experience and machine learning experts that want to iterate quickly.
The advantage of our platform is the speed in which we can demonstrate value. Contact us below to find out how we can help you get your first algorithm up and running.