Anatomopathological Diagnosis of Breast Cancer

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to make an automated —yet totally transparent— medical diagnosis of breast cancer from microscopic images of patient mammary cells?

Goals & benefits

Identify the cellular characteristics involved in cancer and enhance medical knowledge by helping pathologists and oncologists understand the causal relationships between specific cell features, their combination, and the presence of cancer.

Help the medical profession to make earlier and more personalized decisions through rapid, systematic, and explainable diagnoses.

Contribute to improving patient care (pain, survival, duration of treatment) and extend access to high-level diagnoses even in medical deserts


We get a Predictive Model that is:


A Decision System composed of 7 unchained gradual rules, each using some of the 13 variables that XTRACTIS identified as significant, including weak signals (out of 30 Potential Predictors characterizing topological and geometric attributes of cells).


Excellent Real Performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 09/2022 (v2.0)

Results by
XTRACTIS® GENERATE 12.1.42004 (02/2022)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 2 cases
  5. Top-Models Benchmark