| --- |
| license: cc-by-nd-4.0 |
| language: |
| - en |
| base_model: |
| - timm/resnet50.a1_in1k |
| pipeline_tag: image-classification |
| tags: |
| - breast-cancer |
| - ResNet50 |
| - Multiple-instance-learning |
| - Whole-slide-image |
| --- |
| |
| --- |
| license: cc-by-nd-4.0 |
| language: |
| - en |
| base_model: |
| - timm/timm/resnet50.a1_in1k |
| pipeline_tag: image-classification |
| tags: |
| - breast-cancer |
| - whole-slide-image |
| - TCGA-BRCA |
| - Multiple-Instance-Learning |
| --- |
| # ResNet50-MIL for TCGA Breast Cancer |
| |
| This model is a Vision Transformer (ViT) based Multiple Instance Learning (MIL) framework designed for detecting breast cancer in whole slide images (WSI) of surgically removed breast tissue. |
| |
| ## 🏆 Institutional Achievement |
| Developed as part of National HPC Supporting Program by AICA, Gwangju, s.Korea and also partly through |
| National NPU Support Program by NIPA, Jincheon, s. Korea where Elice Group Co., Ltd. in Seoul, s. Korea |
| kindly provided A100 x 2 GPUs for the model training. |
| This model represents our commitment to reducing the manual workload of pathologists through high-performance AI. |
| |
| ## 📊 Model Details |
| - **Architecture:** ResNet50-Backbone with Attention-based MIL Aggregator |
| - **Training Data:** TCGA-BRCA (H&E Stained Slides) |
| - **Framework:** Keras / TensorFlow |
| - **Target:** Detection of breast cancers in surgically removed breast tissue |
| - **Note:** keras_hub utilizes standardized Vision Transformer weights originally researched and released by the Google/timm teams. The base_model tag on Hugging Face is used for lineage tracking. |
| |
| ## Model Reproducibility |
| The implementation, including feature extraction and MIL training, is provided as an interactive Jupyter Notebook in our GitHub repository. This allows researchers to step through the pipeline cell-by-cell. |
| - **Interactive Notebook:** See [MIL_CNN_TCGA.ipynb](https://github.com/kimdesok/ViT-backbone-MIL-on-TCGA/blob/main/MIL_CNN_TCGA.ipynb) |
| - **Environment:** See [requirements.txt](https://github.com/kimdesok/ViT-backbone-MIL-on-TCGA/blob/main/requirements.txt) for dependencies. |
| |
| ## 📁 Dataset & Data Availability |
| The model was trained on a curated version of the **TCGA-BRCA** dataset, processed into 10x patches. |
| |
| ### Dataset Components: |
| - **Patches:** Extracted at 10.0x magnification(for morphological features). |
| |
| ### Access: |
| Due to the significant storage size and ongoing curation for commercial spin-off readiness, the processed dataset is **not publicly hosted** at this time. |
| - **Academic Researchers:** Available upon reasonable request for validation purposes. |
| - **Inquiries:** Please contact [dskim@btrust.co.kr] for data access requests. |
| |
| ## 📊 Dataset Pipeline |
| We provide the full pipeline to convert original TCGA-BRCA's .svs images into the TFRecord format used for training this model. |
| Available at https://github.com/kimdesok/ViT-backbone-MIL-on-TCGA/SVS_to_TFRecord_Convert.ipynb |
|
|
| ### Data Components |
| - **Source:** Original TCGA-BRCA WSIs (.svs) |
| - **Output:** TFRecord sets (10.0x magnification) |
| - **Contents:** Patch sets |
|
|
| ### Accessing the Data |
| The processed TFRecord files are hosted on our secure institutional storage due to their large scale. |
| - **Scripts:** See [here](https://github.com/kimdesok/ViT-backbone-MIL-on-TCGA/SVS_to_TFRecord_Convert.ipynb) for the SVS-to-TFRecord conversion code. |
| - **Download:** To request access to the pre-processed TFRecord sets, please fill out our [Data Request Form/Email us](https://github.com/kimdesok/ViT-backbone-MIL-on-TCGA/tree/main/Data_Access.md). |
|
|
| ## 📈 Version History |
|
|
| | Version | Date | Description | Status | |
| | :--- | :--- | :--- | :--- | |
| | **v1.0** | 2024-05-22 | Initial Release (Fine-tuned on TCGA-BRCA) | Current | |
| | **v2.0** | (TBD) | Planned Virchow 2.0 Integration on H100 | R&D Phase | |
|
|
| ## ⚠️ License & Commercial Use |
| This model is licensed under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**. |
| - **Academics:** Free to use for research and publications. |
| - **Industry/Commercial:** Use for-profit requires a separate commercial license. |
| - **Inquiries:** Please contact [dskim@btrust.co.kr] for licensing and collaboration. |
