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
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
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
Download EfficientNetV2S model
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
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
- Kaggle Competition: Histopathologic Cancer Detection