Popular GitHub Repositories for Brats

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

A collection of resources on applications of Transformers in Medical Imaging.

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

[MICCAI2022] This is an official PyTorch implementation for A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Official Implementation of SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation (CVPRW 2024)

A comprehensive review of techniques to address the missing-modality problem for medical images

Using DCGAN for segmenting brain tumors from brain image scans

[ICIVC 2019] "LSTM multi-modal UNet for Brain Tumor Segmentation"

[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation

A Tensorflow Implementation of Brain Tumor Segmentation using Topological Loss

This is a complete guide on how to do Pyradiomics based feature extraction and then, build a model to calculate the grade of glioma.

Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging

Codebase for Conditioned Diffusion Models for Unsupervised Anomaly Detection

Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf

Creating a U-Net In PyTorch to segment the BraTS 2020 dataset

Code for automated brain tumor segmentation from MRI scans using CNNs with attention mechanisms, deep supervision, and Swin-Transformers. Based on my Master's dissertation project at Brunel University, it features 3 deep learning models, showcasing integration of advanced techniques in medical image analysis.

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

Modified VGG16 and UNetCNN based 4D Image Segmentation (Finalist - Smart India Hackathon 2019)

In this work we present a task-agnostic Multimodal Variational Aversarial Active Learning (M-VAAL) for sampling the most informative samples for annotation in various Medical Image Analysis Downstream tasks, such as segmentation, and classification.

Brain tumor segmentation using YOLOv8 for detection and SAM for precise mask generation on the BraTS 2021 dataset. Includes a Streamlit app for real-time visualization and is ready for deployment.