An art project using generative adversarial network (GAN)

This repository contains implementation of UNET architecture for Image Segmentation.

Implementation of U_Net architecture for medical image segmentation purpose.

Machine Learning

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps reduce the number of premature deaths. In the study, I am working on creating a convolutional neural network capable of identifying tumor areas within medical images (which were taken with ultrasound).

Project on Semantic Segmentation of brain tumor on BraTS'19 Dataset

Image segmentation using U-Net in tensorflow

semantic_segmentation

People with pulmonary disease often have a high opacity, which makes segmentation of the lung from chest X-rays more difficult. In this study, I propose a methodology to improve the performance of the U-NET structure so that it is able to extract the features and spatial characteristics of the X-ray images of the chest region.

recreation of UNET: Convolutional networks for biomedical image segmentation. Implemented with keras & tensorflow.

Image Segmentation using UNET

Tensorflow implementation of U-Net model with TPU Estimator support.

Model to segment 3D MRI images using a 3D UNET based FCN architecture and convert it to a surface mesh. Please see the link below for the full paper.

The project aims to transform the realm of beauty by developing a sophisticated UNet architecture model where the input represents a woman adorned with makeup, while the output showcases her natural, makeup-free beauty.

Find the nuclei in divergent images to using UNet

Tensorflow implementation of UNet on surgical Instrument dataset from laparoscopic videos

Unet-based semantic segmentation for pet images in TensorFlow using the Oxford-IIIT Pet Dataset.

Tensorflow based framework for 3D-Unet with Knowledge Distillation

Identify glaciers in satellite images with a U^2-Net

In this project, we analyzed a video recording of a vehicle driving on the road and computed semantic segmentation for each frame in the video.