Machine learning for NeuroImaging in Python

Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

Code for "Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs"

Deep Learning tools for brain medical images

Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net

Code for DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

Smart India Hackathon 2019 project given by the Department of Atomic Energy

The project is used to do preprocessing on brain MR images by using Nipype.

[ECCV 2024] UMBRAE: Unified Multimodal Brain Decoding | Unveiling the 'Dark Side' of Brain Modality

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.

Anomaly localization using autoencoder models in the feature space of a ResNet

Preprocessed IXI brain MRI dataset with subcortical segmentation

Brain T1-Weighted MRI Images Classification and WGAN Generation (Alzheimer's and Healthy patients) for the purpose of data augmentation. Implemented in TensorFlow, trained on ADNI dataset.

[ECCV 2024] BrainHub: Multimodal Brain Understanding Benchmark

A complete pipelined automatic process for skull stripping and tumor segmentation from Brain MRI using Thresholding.

[TCybern2019] Brain MRI Super-Resolution based on Thick-Section MR Images from Two Planes.

Code for "Deep Attention Super-Resolution of Brain Magnetic Resonance Images Acquired Under Clinical Protocols".

Code for the paper "Contrastive learning for regression in multi-site brain age prediction" | ISBI 2023 https://doi.org/10.1109/ISBI53787.2023.10230733

A Collection of Data sets and Approaches to UAD in Brain MRI.

Based on our paper on Multi-Objective Harris Hawk's Optimization with Altruism for Unsupervised Brain MRI Segmentation