Anatomopathological Diagnosis Of Breast Cancer

Benchmark vs. Logistic Regression, Random Forest, Boosted Tree & Neural Network

Design an AI-based decision system that accurately and instantly makes a rational 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.

  • The top-model is a decision system composed of 7 disjunctive gradual rules without chaining aggregated into 2 disjunctive rules.
  • Each rule uses from 2 to 5 predictors among the 13 variables that XTRACTIS automatically identified as significant (out of the 30 attributes of mammary cells describing each image).
  • Only a few rules are triggered at a time to compute the decision.

It has an excellent Real Performance (on unknown data).

It computes real-time predictions up to 70,000 decisions/second, offline or online (API).

UC04 scores graph

LoR=Logistic Regression
RFo=Random Forest
BT=Boosted Tree
NN=Neural Network

Detailed results and explanations in full document

Use Case 2024/03 (v3.1)

Powered by XTRACTIS® REVEAL v12.1.42004 (2022/02)


  1. Problem Definition
  2. XTRACTIS-induced Decision System
  3. XTRACTIS Process
  4. Top-Model Induction
  5. Explained Predictions for 2 unkown cases
  6. Top-Models Benchmark
  7. Appendices