Inter-vertebral disc modelling Using pre-processing networks based on deep ResUNet

ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models.

Implementation of ResUnet++ using Tensorflow 2.0.

Use ResNet50 deep learning model to predict defects in steel and visually localize the defect using Res-UNET model class

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

Implementation of the paper "ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data" in TensorFlow.

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

The project implements a ResNet to detect brain tumours from MRI images and then uses ResUNet model to perform localization of the identified brain tumours.

Step by Step ResUnet Model Architecture using Keras

Brain Tissue Segmentation on IBSR18 Dataset

Implements Deep Residual U-Net network.

Lung segmentation for chest X-Ray images with ResUNet and UNet. In addition, feature extraction and tuberculosis cases diagnosis had developed.

Deep learning models to estimate the masses of galaxy clusters from lensed CMB maps

Semantic Segmentation for deforestation in Bolivia.

Applying AI using deep learning, in specific ResNet & ResUNet to classify brain tumors images.

Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.

Nuclei Segmentation using ResUNet

single image super resolution

The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

This project uses deep learning to detect and localize brain tumors from MRI scans. It uses a ResNet50 model for classification and a ResUNet model for segmentation. It evaluates the models on a dataset of LGG brain tumors.