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---
language: en
license: mit
tags:
- deep-learning
- cancer-detection
- histopathology
- tensorflow
- efficientnet
- vision-transformer
- ViT
- medical-imaging
model_name: EfficientNetV2S & ViT-Hybrid for Histopathologic Cancer Detection
library_name: tensorflow
datasets:
- histopathologic-cancer-detection
- PatchCamelyon
pipeline_tag: image-classification
---
# Histopathologic Cancer Detection - EfficientNetV2S & ViT-Hybrid
This repository contains models for detecting metastatic cancer in histopathologic images.
- **EfficientNetV2S**: A Baseline CNN-based model for local feature extraction.
- **ViT-Hybrid**: A Transformer-based model that learns global dependencies.
Both models were trained on the [Histopathologic Cancer Detection Kaggle dataset](https://www.kaggle.com/competitions/histopathologic-cancer-detection/data)
## Model Performance
- **EfficientNetV2S**
- Accuracy: 93.59% (Private), 93.74% (Public)
- AUC: 0.9774
- **ViT-Hybrid**
- Accuracy: 95.07% (Private), 94.87% (Public)
- AUC: 0.9791
- **ViT-Hybrid + TTA (Test-Time Augmentation)**
- Accuracy: 96.50% (Private), 96.75% (Public)
## Model Use
```sh
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
```
# Download EfficientNetV2S model
```sh
model_path = hf_hub_download(repo_id="MooseML/EfficientNet-Cancer-Detection", filename="efficientnet_cancer_model.h5")
model = load_model(model_path)
```
# Download ViT-Hybrid model
```sh
model_path_vit = hf_hub_download(repo_id="MooseML/EfficientNet-Cancer-Detection", filename="ViT_hybrid_cancer_model.h5")
model_vit = load_model(model_path_vit)
```
## Github and Kaggle Links for Full Training Pipeline
- Full Training Code: [GitHub Repository](https://github.com/MooseML/Histopathologic-Cancer-Detection)
- Kaggle Competition: [Histopathologic Cancer Detection](https://www.kaggle.com/competitions/histopathologic-cancer-detection)
---
license: mit
--- |