Segmentation models with pretrained backbones. PyTorch.

🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation 🍞🍖🍕

keras implementation of unet plus plus

Decoder architecture based on the UNet++. Combining residual bottlenecks with depthwise convolutions and attention mechanisms, it outperforms the UNet++ in a coronary artery segmentation task, while being significantly more computationally efficient.

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for voxel-level clinically significant prostate cancer detection in multi-channel 3D bpMRI scans.

🏥 医学图像作业：图像配准论文阅读；眼底血管分割实验

Logiciel de détection, localisation & segmentation de carries sur des radiographies dentaires.

Clean Implementation of Unet3+ and validation on Cityscapes dataset.

UNet++, UNet, SegNet and DeepLabv3 implemented in Keras for MoNuSeg dataset

3D car detection competition on Kaggle : https://www.kaggle.com/c/pku-autonomous-driving/overview

Brain tumor segmentation using UNet++ Architecture . Implementation of the paper titled - UNet++: A Nested U-Net Architecture for Medical Image Segmentation @ https://arxiv.org/abs/1807.10165

Implementation of UNET++ for CAC Scoring using Tensorflow

[🥉 3rd place] AI-powered web application able to track changes in the urban landscape

Model for Doctor's Cyclop Project at FPT Edu Hackathon 2021

This repository contains the image classification followed by semantic segmentation of Chest X-Rays to detect a clinical condition called Pneumothorax.

UNet and UNet++ implemented in Tensorflow

Brain MRI segmentation using segmentation models

The repository contains the reproduction of UNetPluPlus, introduced by Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang; upon the dataset obtained from the KITS19 Kidney Tumor Segmentation Challenge.

Training code for Vesuvius Kaggle Competition

Task to create a model that can automatically segment the stomach and intestines on MRI scans.