A PyTorch baseline 3D Unet model to segment blood vessels in 3D images of kidneys. Trained in the SenNet + HOA Kaggle competition.
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Updated
Apr 8, 2024 - Jupyter Notebook
A PyTorch baseline 3D Unet model to segment blood vessels in 3D images of kidneys. Trained in the SenNet + HOA Kaggle competition.
MICCAI2019: 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Iterative Vertebrae Segmentation - VerSe dataset
Tensorflow based framework for 3D-Unet with Knowledge Distillation
Using the BraTS2020 dataset, we test several approaches for brain tumour segmentation such as developing novel models we call 3D-ONet and 3D-SphereNet, our own variant of 3D-UNet with more than one encoder-decoder paths.
Using synthetic datasets to train an end-to-end CNN for 3D fault segmentation
3D segmentation of neurites in EM images.
An image enhancement and segmentation pipeline for generating connectomic reconstructions from X-ray holographic nanotomography, using CycleGANs, Local Shape Descriptors, and Mutex Watershed. Built with PyTorch, Daisy, and Gunpowder.
Fully automatic brain tumor segmentation using the Modified 3DUNet architecture for Brats 2020 Challenge.
Medical images segmentation with 3D UNet GAN
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Segmentation of thoracic and lumbar spine using deep learning
Implementation of DiffusionOverDiffusion architecture presented in NUWA-XL in a form of ControlNet-like module on top of ModelScope text2video model for extremely long video generation.
The U-Net Segmentation server (caffe_unet) for Docker
Urban change model designed to identify changes across 2 timestamps
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