Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation. (using diffusion for 3D medical image segmentation)

Multimodal Brain mpMRI segmentation on BraTS 2023 and BraTS 2021 datasets.

A complete pipeline for BraTS 2020

3d unet and 3d autoencoder for automatical segmentation and feature extraction.

Training of Noise-to-Image Diffusion Model on Multi-Channel Brain Tumor MRI Scans.

Brain tumor segmentation

Using the BraTS2020 dataset, we test several approaches for brain tumour segmentation such as developing novel models we call 3D-ONet and 3D-SphereNet, our own variant of 3D-UNet with more than one encoder-decoder paths.

Brain Tumor Image segmentation-Brats2019, 2020, 2021

🧠 3D Brain Tumor Segmentation with MONAI | BRATS 2020 Baseline UNet, UNet++, SegResNet 🚀

https://www.youtube.com/watch?v=0h1eezoZhEU&t=26s

3D Brain Tumor Segmentation using U-Net and MONAI on the BraTS 2020 dataset to optimize segmentation performance across multiple MRI modalities.

A deep learning project for brain tumor classification and segmentation on MRI images using CNN, U-Net, and VIT models.

discusses deep learning models for segmenting MRI images, specifically the UNET model for Brain Tumor Segmentation

End-to-end brain tumor segmentation on BraTS2020 with a modified DUCKNet (U-Net + DenseNet). Includes data preprocessing/augmentation, Keras training loops, and rigorous eval (Dice/IoU), achieving 88.7% validation Dice with 0.010 validation loss. Reproducible notebook and comparisons vs baseline U-Net; trained on A100.

This project uses Gabor filters and 3D U-Net to detect and segment brain tumors from MRI scans using the BraTS 2020 dataset

Conducting multimodal semantic segmentation of brain tumor using 3D U-Net

Multimodal Brain Tumor Segmentation

PyTorch 2D U-Net for BraTS brain tumor segmentation (FLAIR+T1CE) with per-modality p99 normalization, CE+SoftDice loss, solid metrics, and a prediction visualizer.

Brain Tumour Segmentation with TrUE-Net tool - top 10 DL model in MICCAI BraTS 2020

3D U-Net for brain tumor segmentation on BraTS 2020 (.nii) data, adapted for interactive use with .npy files via a pre-trained model and Gradio interface. Includes preprocessing and custom architecture.

Deep Learning with CNNs course project

PyTorch-based multimodal brain tumor segmentation using BraTS 2020, featuring vanilla, residual, and attention-guided U-Net architectures with comprehensive W&B experiment tracking.

Glioblastoma 3D Segmentation with nnU-Net and Patch Learning.

No description provided.

No description provided.

An enhanced version of UNet with Atrous Spatial Pooling and Attention Gates for Brain Tumor Segmentation with BraTS 2020 dataset.

Brain tumor detection and segmentation from 3D MRI scans using a 3D U-Net model. Includes preprocessing, training, and evaluation on a subset of BraTS 2020 Training-Validation dataset, plus an interactive Streamlit app for uploading MRI scans, visualizing 3D segmentation results, and exploring model predictions.

Попытка реализовать сегментацию опухоли мозга используя набор BraTS_2020