XTRACTIS FOR PREDICTIVE MEDECINE
ANATOMOPATHOLOGICAL DIAGNOSIS OF BREAST CANCER
Benchmark: XTRACTIS VS. RANDOM FORESTS, BOOSTED TREES & NEURAL NETWORKS
This study aims to solve the following problem:
How to make an automated medical diagnosis of breast cancer from microscopic images of patient tumors?
- Enhance medical knowledge by helping pathologists and oncologists understand the causal relationships between mammal cell characteristics under the microscope and the presence of cancer.
- Assist the medical profession in making an earlier and more individualized decision, thanks to rapid and systematic diagnoses.
- Contribute to improving patient care (pain, survival, duration of treatment) and access to diagnostics even in medical deserts.
- Intelligible. 7 gradual unchained rules, based on 13 predictors.
- Robust. Excellent predictive performance.
- Efficient. Instant prediction (online or offline).
- Problem Definition
- Xtractis Solution
- Top-Model Induction
- Prediction for 2 Unknown Cases of Testing
- Top-IVE Benchmark