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

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

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

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.

Official Implementation for SEDNet

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

Deep Learning with CNNs course project

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

Multimodal Brain Tumor Segmentation

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

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