zhixuhao/unet
Unet
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.
Features
- Biomedical Image Segmentations - Provides a deep neural network architecture specifically for generating precise pixel-level masks of biomedical anatomical structures.
- Convolutional Neural Networks - Implements a convolutional neural network using an encoder-decoder path for spatial information extraction.
- Symmetric Encoder-Decoders - Implements a symmetric encoder-decoder architecture to capture global context and enable precise pixel-level localization.
- Skip-Connection Fusion - Employs skip-connections to fuse high-resolution contracting path features with upsampled outputs for precise localization.
- Computer Vision Segmentation Models - Provides a PyTorch-based computer vision model for pixel-level object isolation and segmentation.
- Medical Image Segmentations - Creates precise pixel-level masks to isolate anatomical structures in medical scans using a deep neural network.
- U-Net Architectures - Implements the U-Net architecture for generating high-precision pixel masks in medical imagery.
- Biomedical Image Processing Toolkits - Processes and transforms biomedical imagery to facilitate the extraction of meaningful anatomical features.
- Neural Image Processors - Functions as a neural network-based image processor for biomedical segmentation and dataset expansion.
- Semantic Segmentation Training - Optimizes a semantic segmentation model using binary cross-entropy loss for accurate mask generation.
- Convolutional Block Composers - Utilizes modular convolutional blocks containing convolutions and max-pooling for hierarchical feature extraction.
- Image Augmentations - Applies image transformations including cubic interpolation, rotation, and flipping to expand the training dataset.
- Transposed Convolutions - Uses transposed convolutions to upsample feature maps and reconstruct the final image mask resolution.
- Computer Vision - Implementation of the U-Net architecture for image segmentation.
- Segmentation Architectures - U-Net implementation for image segmentation tasks.
Star history
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