This repository contains the code for semantic segmentation of the retina blood vessel on the DRIVE dataset using the PyTorch framework.

This the repo for the paper tiltled "AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation"

U-Net for person segmentation in TensorFlow using Keras API.

RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation (MICCAI 2019)

This repository contains the code for the Retina Vessel Segmentation (DRIVE) using the UNET architecture in TensorFlow 2.0

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg

Segmentation models for 3D data with different backbones. PyTorch.

This repository contains the code for Lung segmentation using Montgomery dataset in TensorFlow 2.0.

Semantic segmentation on earthquake data with U-net

PyTorch implementation of Original UNet Paper

A computer vision project (image segmentation project) which aims to remove texts on images using Unet model. Tensorflow 2 is used as a ML library.

The official repository for CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks.

Tensorflow implementation of UNet on surgical Instrument dataset from laparoscopic videos

Detection of Cigarette buts on the streets using U-net segmentation.

Semantic Segmentation project for Autonomous Driving based on a TensorFlow implementation of UNet

deep learning approach to pixel-wise classification by running it through the UNet encoder, then utilizing a combination of Convolutional Neural Networks (CNN) and an attention map to specifically observe the significant region of the ultrasonic image, and finally run it through the Unet decoder.

Iterative Vertebrae Segmentation - VerSe dataset

A modular, 3D unet built in keras for 3D medical image segmentation. Also includes useful classes for extracting and training on 3D patches for data augmentation or memory efficiency.

Early exploration of detecting signs of lung pathology in COVID-19 Lung CT cases

Testing Mixed Precision Training Using Pytorch Lightning and Segmentation Models