LeeJunHyun/Image_Segmentation
Image Segmentation
This project is a biomedical image segmentation framework and PyTorch computer vision library. It provides a deep learning pipeline for isolating specific anatomical structures within medical imagery using pixel-level binary classification.
The system utilizes an encoder-decoder neural architecture combined with attention-based feature refinement to highlight relevant anatomical regions and suppress background noise.
The toolkit covers a full training workflow, including stochastic data augmentation for biomedical datasets, hyperparameter optimization, and model persistence for restoring pretrained weights. It also includes evaluation tools to verify segmentation accuracy using similarity coefficients and precision metrics against ground truth masks.
Features
- Medical Image Segmentations - Provides a specialized framework for isolating anatomical structures in medical imagery using deep learning.
- Segmentation Model Training - Provides a complete pipeline for executing training loops to learn anatomical image segmentation masks.
- Deep Learning Pipelines - Implements a deep learning pipeline for training and optimizing networks to identify medical image regions.
- Encoder-Decoder Architectures - Utilizes an encoder-decoder neural architecture to process image features and generate high-resolution pixel masks.
- Neural Network Training - Provides a complete training workflow from weight initialization through optimization loops and hyperparameter tuning.
- Image Segmentations - Constructs neural network architectures designed to partition biomedical images into anatomical regions.
- PyTorch Semantic Segmentation Libraries - Offers a PyTorch library with specialized encoders and decoders for pixel-level biomedical image classification.
- Medical Imaging Training Frameworks - Provides a PyTorch-based framework for isolating anatomical structures using attention-based neural networks.
- Pixel-Level Classifiers - Implements pixel-level binary classification to delineate boundaries between anatomical structures.
- Biomedical Image Processing Toolkits - Ships a toolkit for augmenting medical datasets and evaluating segmentation mask similarity.
- Spatial Attention Weighting - Implements spatial attention weighting to emphasize relevant anatomical regions and suppress background noise.
- Biomedical Data Augmentation - Implements medical-specific data augmentation using rotations and cropping to improve model robustness.
- Image Augmentation - Increases dataset variety through random rotations, cropping, flipping, and color jittering.
- Segmentation Model Validation - Measures the accuracy of segmentation predictions using similarity coefficients and precision metrics.
- Image Augmentation Transforms - Applies random geometric and color transformations to increase dataset diversity and prevent overfitting.
- Segmentation Metrics - Provides quantitative measurements using similarity coefficients and precision metrics to evaluate pixel-level segmentation accuracy.
- Segmentation Evaluation Metrics - Evaluates segmentation accuracy using similarity coefficients and intersection-over-union metrics against ground truth.
- Model Generalization Verifications - Tests trained architectures against validation and test datasets to ensure accuracy and generalizability.
Star history
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