zhixuhao/unet
Unet
Dieses Projekt ist eine PyTorch-Implementierung eines U-Net-konvolutionalen neuronalen Netzwerks, das für die pixelgenaue Bildsegmentierung entwickelt wurde. Es fungiert als biomedizinischer Bildprozessor, der präzise Masken generiert, um anatomische Strukturen innerhalb medizinischer Bilder zu isolieren.
Die Architektur nutzt eine symmetrische Encoder-Decoder-Struktur, um Kontext zu erfassen und eine präzise Lokalisierung zu ermöglichen. Sie verwendet Feature-Fusion durch Skip-Connections, um hochauflösende Merkmale aus dem kontrahierenden Pfad mit hochskalierten Ausgaben zu kombinieren und so räumliche Details wiederherzustellen.
Das System deckt das Training von Deep-Learning-Modellen mittels binärer Cross-Entropy-Loss ab und integriert Datenerweiterungstechniken wie kubische Interpolation, Rotation und Spiegelung, um die Vielfalt des Trainingsdatensatzes zu erweitern.
Die Implementierung wird als Jupyter Notebook bereitgestellt.
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.
