LN Segmentation

--- license: mit tags: - image-segmentation - multilabel - unet - pytorch - medical-imaging library_name: transformers pipeline_tag: image-segmentation --- # LN_segmentation A unet model for multilabel image segmentation trained with sliding window approach. ## Model Description - **Architecture:** unet - **Input Channels:** 3 - **Output Classes:** 4 - **Base Filters:** 32 - **Window Size:** 256 ### Model-Specific Parameters ## Training Configuration | Parameter | Value | |-----------|-------| | Batch Size | 64 | | Learning Rate | 0.0003 | | Weight Decay | 0.01 | | Epochs | 100 | | Patience | 10 | | Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 | ## Performance Metrics | Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 | |--------|------|--------|--------|--------|--------| | Dice | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 | | IoU | 0.4059 | 0.0989 | 0.1749 | 0.5612 | 0.7887 | | F1 | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 | | MCC | 0.5044 | 0.1730 | 0.2861 | 0.7032 | 0.8554 | | ROC AUC | 0.8338 | 0.6482 | 0.7772 | 0.9252 | 0.9847 | | PR AUC | 0.4846 | 0.0767 | 0.1807 | 0.7583 | 0.9227 | ## Usage ```python import numpy as np from model import MODEL_REGISTRY, SegmentationConfig # Load model config = SegmentationConfig.from_pretrained("aholk/LN_segmentation") model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation") model.eval() # Run inference on a full image with sliding window image = np.random.rand(2048, 2048, 3).astype(np.float32) # Your image here probs = model.predict_full_image( image, dim=256, batch_size=16, device="cuda" # or "cpu" ) # probs shape: (num_classes, H, W) with values in [0, 1] # Threshold to get binary masks masks = (probs > 0.5).astype(np.uint8) ``` ## Training Plots     ## Citation If you use this model, please cite: ```bibtex @software{windowz_segmentation, title={Multilabel Image Segmentation with Sliding Window U-Net}, author={Gleghorn Lab}, year={2025}, url={https://github.com/GleghornLab/ComputerVision2} } ```