Input
Gene expression data from 537 patients
NeoPulse®’s training configuration
- Input: 20,531 gene expression data
Output: the probability of cancer
- Number of generated models: 4
- The architecture of Deep Learning algorithm: auto
Results
A lung carcinoma diagnostics model with high performance was built, using gene expression data.
- Some models showed 100% accuracy with test data
- AUC score: 0.97 ± 0.053
Confusion matrix
ROC
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.
Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography