| --- |
| 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 |
|
|
