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