Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

🍔🍟🍗 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.

🏥 医学图像作业：图像配准论文阅读；眼底血管分割实验

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.

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

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.

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

UNet and UNet++ implemented in Tensorflow

Brain MRI segmentation using segmentation models

Architecture implemntation of various Unet models with pre-trained backbones, UNet++ model with pretrained backbone on Medical dataset

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