Instructions to use ViettNguyen21/brain_msi_segmentation_effunet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ViettNguyen21/brain_msi_segmentation_effunet with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://ViettNguyen21/brain_msi_segmentation_effunet") - Notebooks
- Google Colab
- Kaggle
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Verified signature# 🧠 Brain Tumor Segmentation — BRISC2025 > Binary segmentation of brain tumors from MRI scans using DeepLabV3+ (MobileNetV2) and EfficientNet-B1 U-Net with two-stage transfer learning. [](https://tensorflow.org) [](https://python.org) [](LICENSE) [](https://brisc2025.grand-challenge.org) --- ## 📋 Table of Contents - [Overview](#overview) - [Dataset](#dataset) - [Models](#models) - [Results](#results) - [Project Structure](#project-structure) - [Installation](#installation) - [Usage](#usage) - [Training](#training) - [Evaluation](#evaluation) - [Citation](#citation) --- ## Overview This project tackles **binary brain tumor segmentation** on MRI scans from the BRISC2025 challenge. Given an MRI image, the model …e03eea2 -
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