🫁 Automate ETT and Carina segmentation on chest radiographs for faster, accurate assessments, improving patient care and treatment efficiency.

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

Code Implementation of the article "A Framework for Real-Time Volcano-Seismic Event Recognition Based on Multi-Station Seismograms and Semantic Segmentation Models" (under review).

使用UNet和UNet++以及ResNet-34对ISBI数据集进行训练，可配置SGD和Adam两种优化器，提供测试程序将测试集图像进行预测，每次训练结束保存模型权重参数和损失、精度的图表

Implements a UNet-based medical image segmentation framework for precise detection of the carina and endotracheal tube tip, supporting automated clinical evaluation of airway placement.

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

PyTorch implementation of medical semantic segmentations models, e.g. UNet, UNet++, DUCKNet, ResUNet, ResUNet++, and support knowledge distillation, distributed training, Optuna etc.

This study focuses on the performance of U-Net++ for hotspot segmentation on bone scan images. This code is proprietary and is shared for reference purposes only. It may not be used, modified, or distributed without explicit permission from the author.

An open-source UNet-based pipeline for nuclei segmentation in histopathology images using the PanNuke dataset. It features an interactive web app for easy data visualization and handling, making AI tools accessible even for non-experts. This project provides a foundation for training and exploring histopathology data.

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

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

Implementation of UNET++ for CAC Scoring using Tensorflow

Training code for Vesuvius Kaggle Competition

Clean Implementation of Unet3+ and validation on Cityscapes dataset.

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

Brain MRI segmentation using segmentation models

UNet and UNet++ implemented in Tensorflow

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.

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.

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