# zhixuhao/unet

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4,928 stars · 2,010 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/zhixuhao/unet
- awesome-repositories: https://awesome-repositories.com/repository/zhixuhao-unet.md

## Topics

`keras` `segmentation` `unet`

## Description

This project is a PyTorch implementation of a U-Net convolutional neural network designed for pixel-level image segmentation. It functions as a biomedical image processor that generates precise masks to isolate anatomical structures within medical imagery.

The architecture utilizes a symmetric encoder-decoder structure to capture context and enable precise localization. It employs skip-connection feature fusion to combine high-resolution features from the contracting path with upsampled outputs, recovering spatial detail.

The system covers deep learning model training using binary cross-entropy loss and incorporates data augmentation techniques, such as cubic interpolation, rotation, and flipping, to expand training dataset variety.

The implementation is provided as a Jupyter Notebook.

## Tags

### Graphics & Multimedia

- [Biomedical Image Segmentations](https://awesome-repositories.com/f/graphics-multimedia/biomedical-image-processing-toolkits/biomedical-image-segmentations.md) — Provides a deep neural network architecture specifically for generating precise pixel-level masks of biomedical anatomical structures. ([source](https://cdn.jsdelivr.net/gh/zhixuhao/unet@master/README.md))
- [Biomedical Image Processing Toolkits](https://awesome-repositories.com/f/graphics-multimedia/biomedical-image-processing-toolkits.md) — Processes and transforms biomedical imagery to facilitate the extraction of meaningful anatomical features.
- [Neural Image Processors](https://awesome-repositories.com/f/graphics-multimedia/neural-image-processors.md) — Functions as a neural network-based image processor for biomedical segmentation and dataset expansion.

### Artificial Intelligence & ML

- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Implements a convolutional neural network using an encoder-decoder path for spatial information extraction.
- [Symmetric Encoder-Decoders](https://awesome-repositories.com/f/artificial-intelligence-ml/encoder-decoder-architectures/symmetric-encoder-decoders.md) — Implements a symmetric encoder-decoder architecture to capture global context and enable precise pixel-level localization.
- [Skip-Connection Fusion](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-fusion-architectures/skip-connection-fusion.md) — Employs skip-connections to fuse high-resolution contracting path features with upsampled outputs for precise localization.
- [Computer Vision Segmentation Models](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/computer-vision-segmentation-models.md) — Provides a PyTorch-based computer vision model for pixel-level object isolation and segmentation.
- [Medical Image Segmentations](https://awesome-repositories.com/f/artificial-intelligence-ml/medical-image-segmentations.md) — Creates precise pixel-level masks to isolate anatomical structures in medical scans using a deep neural network.
- [U-Net Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures/u-net-architectures.md) — Implements the U-Net architecture for generating high-precision pixel masks in medical imagery.
- [Semantic Segmentation Training](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/image-segmentation/segmentation-model-training/semantic-segmentation-training.md) — Optimizes a semantic segmentation model using binary cross-entropy loss for accurate mask generation. ([source](https://cdn.jsdelivr.net/gh/zhixuhao/unet@master/README.md))
- [Convolutional Block Composers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/convolution-layers/convolutional-block-composers.md) — Utilizes modular convolutional blocks containing convolutions and max-pooling for hierarchical feature extraction.
- [Image Augmentations](https://awesome-repositories.com/f/artificial-intelligence-ml/training-data-transformations/image-augmentations.md) — Applies image transformations including cubic interpolation, rotation, and flipping to expand the training dataset.
- [Transposed Convolutions](https://awesome-repositories.com/f/artificial-intelligence-ml/transposed-convolutions.md) — Uses transposed convolutions to upsample feature maps and reconstruct the final image mask resolution.

### Part of an Awesome List

- [Computer Vision](https://awesome-repositories.com/f/awesome-lists/ai/computer-vision.md) — Implementation of the U-Net architecture for image segmentation.
- [Segmentation Architectures](https://awesome-repositories.com/f/awesome-lists/ai/segmentation-architectures.md) — U-Net implementation for image segmentation tasks.