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.
Using synthetic datasets to train an end-to-end CNN for 3D fault segmentation
3D segmentation of neurites in EM images.
MICCAI2019: 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
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.
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.
Iterative Vertebrae Segmentation - VerSe dataset
Segmentation of thoracic and lumbar spine using deep learning
Fully automatic brain tumor segmentation using the Modified 3DUNet architecture for Brats 2020 Challenge.
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Medical images segmentation with 3D UNet GAN
The U-Net Segmentation server (caffe_unet) for Docker
Urban change model designed to identify changes across 2 timestamps
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.
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