README.md

metadata

title: Pcam Project
emoji: 🧬
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 5.34.1
app_file: app.py
pinned: false
license: gpl-3.0
short_description: 'PCam Dataset: Tumor Detection Image Binary Classification'

🧬 PCam Dataset: Tumor Detection via Binary Image Classification

⚡ Try it now ! With gradio ⚡

On Hugging Face Spaces:

Or start the local gradio app

python app.py

The full pytorch training jupter notebook is here:

You can view it here :

Or execute it on kaggle:

📊 Dataset Overview

https://github.com/basveeling/pcam

The PatchCamelyon (PCam) benchmark is a challenging image classification dataset designed for breast cancer detection tasks.

• 📦 Total images: 327,680 color patches
• 🖼️ Image size: 96 × 96 pixels
• 🧪 Source: Histopathologic scans of lymph node sections
• 🏷️ Labels: Binary — A positive (1) label indicates that the center 32x32px region of a patch contains at least one pixel of tumor tissue. Tumor tissue in the outer region of the patch does not influence the label.

B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962

Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585

Under CC0 License

Results

The submission on kaggle with the model trained on this notebook is

Public score: 0.9733