---
language:
- en
tags:
- medical
- histopathology
- breast-cancer
- vision
- multiple-instance-learning
- pytorch
- pathology
license: cc-by-nc-4.0
datasets:
- bracs
- bach
- breakhis
metrics:
- accuracy
- f1
- recall
---

# OpenPink Core (Breast Cancer Histopathology AI)

OpenPink Core is a state-of-the-art Dual-Stream Multiple Instance Learning (MIL) model designed for the classification of Breast Cancer Whole Slide Images (WSIs) and Regions of Interest (ROIs). Built on top of the **Virchow2** foundation model, OpenPink classifies tissue into four clinical categories: **Atypical, Benign, Malignant, and Normal**, with a specific emphasis on identifying diagnostically challenging Atypical lesions.

## Model Details

- **Architecture:** Dual-Stream Gated Attention MIL (processes global macroscopic thumbnails alongside high-resolution 224x224 patches).
- **Feature Extractor:** Virchow2 (1280-dimensional embeddings, SwiGLU MLP, SiLU).
- **Training Strategy:** 10-Fold Stratified Group Cross-Validation.
- **Domain Adaptation:** Multi-source training utilizing Hard Example Mining from South American (BreaKHis) and European (BRACS, BACH) datasets to decouple cellular density from malignancy.
- **Ensemble:** The repository provides 10 pre-trained checkpoints (folds) for robust ensemble inference.
- **License:** CC-BY-NC-4.0 (Strictly Non-Commercial & Research use only).

## Intended Use & Limitations

**FOR ACADEMIC AND RESEARCH PURPOSES ONLY.**

- **Not for Clinical Use:** This tool has not been evaluated by the FDA, EMA, or any other regulatory body. It is not a medical device. It cannot be used for diagnostic, prognostic, or therapeutic purposes.
- **Limitations:** The model assumes H&E (Hematoxylin and Eosin) stained slides. Variations in extreme staining protocols may degrade performance. We strongly recommend applying Macenko Stain Normalization prior to feature extraction. Note: An automated Macenko normalization pipeline is included directly in the OpenPink-Core repository for your convenience.

## Training Data

The model was trained and internally validated on multi-centric European clinical cohorts:
- **BRACS (BReAst Carcinoma Subtyping):** Hematoxylin & Eosin (H&E) stained ROIs.
- **BACH (Breast Cancer Histology):** High-resolution microscopy images.

To ensure global generalization, the model was aggressively domain-adapted using "Hard Negative" injections from the **BreaKHis** dataset (Brazil), focusing on hyper-cellular benign tumors like Fibroadenomas to prevent False Positives in dense tissue structures.

## Evaluation Results

### Internal Validation (European Cohorts)
During 10-fold CV on BRACS and BACH, the model achieved:
- **Accuracy:** 93.33%
- **Macro F1-Score:** 90.18%
- **Atypical Recall (Sensitivity):** 95.65%
- **Malignant Recall:** 98.68%

### Out-Of-Distribution (OOD) Domain Adaptation
Evaluated on **1,693 unseen images** from BreaKHis using a strict Clinical Thresholding constraint (Probability > 0.30 for Malignant).
- **Malignant Recall:** 82.46% (945 / 1146 cases)
- **Benign Specificity:** 66.73% (365 / 547 cases) - *Massive improvement over the 16.8% baseline.*
- **Perfect Separation:** Zero Atypical or Normal misclassifications in the entire OOD set.

## How to Use

The checkpoints in this repository are designed to be loaded by the `OpenPink-Core` inference scripts. 

### 1. Download Checkpoints via Python
You can automatically download all 10 folds using the `huggingface_hub` library:

```python
from huggingface_hub import snapshot_download

# Download the checkpoints folder
snapshot_download(
    repo_id="MatthewMak/OpenPink-Core", 
    allow_patterns="*.pt", 
    local_dir="./checkpoints"
)
```

### 2. Feature Extraction & Inference
Clone the [OpenPink-Core GitHub/GitLab Repository](#) (link pending) and run the evaluation scripts using the downloaded checkpoints.

```bash
# 1. Extract Virchow2 Features (Auto-Macenko enabled)
python extract_features.py --data_dir ./images --output_dir ./features --ref_image ref.png

# 2. Run Ensemble Inference with Clinical Thresholding
python evaluate_ood.py --features_dir ./features --checkpoints_dir ./checkpoints --malignant_threshold 0.30
```

## Citation & Credits
- **Author/Lead Researcher:** Matthaios Makrogiannis
- **Institution:** University of Ioannina
- **Year:** 2026