Skin Lesion Segmentation Unet

--- license: mit language: - en tags: - medical-image-segmentation - skin-lesion-segmentation - semantic-segmentation - semi-supervised-learning - pseudo-labeling - pytorch - resnet34 - unet --- dataset: - ISIC 2018 # Teacher–Student Pseudo-Labeling for Semi-Supervised Skin Lesion Segmentation This repository contains the pretrained model checkpoints accompanying the paper: > **Teacher–Student Pseudo-Labeling for Semi-Supervised Skin Lesion Segmentation: A Reproducible Baseline** The models implement a teacher–student pseudo-labeling framework for semantic segmentation of dermoscopic skin lesions using a U-Net architecture with a pretrained ResNet-34 encoder. --- # Overview Pixel-wise annotation of medical images is expensive and requires expert dermatologists. This work investigates a simple yet effective semi-supervised learning strategy that leverages unlabeled dermoscopic images through teacher-generated pseudo labels. The repository provides pretrained checkpoints for: - Fully supervised baseline - Teacher model (20% labeled data) - Student model (20% labeled + pseudo labels) - Teacher model (50% labeled data) - Student model (50% labeled + pseudo labels) The implementation is fully reproducible and was trained using free Kaggle Tesla T4 GPU resources. --- # Model Architecture - Architecture: U-Net - Encoder: ResNet-34 (ImageNet pretrained) - Framework: PyTorch - Image Size: 256 × 256 - Task: Binary semantic segmentation - Dataset: ISIC 2018 Skin Lesion Segmentation Challenge --- # Available Checkpoints | Model | Description | |--------|-------------| | `best_full_supervised_model.pth` | Fully supervised baseline trained using 100% labeled images | | `teacher_20pct.pth` | Teacher model trained using only 20% labeled images | | `student_20pct.pth` | Student model trained using 20% labeled images and pseudo labels | | `teacher_50pct.pth` | Teacher model trained using only 50% labeled images | | `student_50pct.pth` | Student model trained using 50% labeled images and pseudo labels | --- # Performance ## Fully Supervised Baseline | Metric | Score | |--------|------:| | Dice | **0.7769** | | IoU | 0.6482 | | Pixel Accuracy | 0.9130 | --- ## Semi-Supervised Results | Experiment | Best Validation Dice | |------------|--------------------:| | Teacher (20%) | 0.7570 | | Student (20%) | **0.7638** | | Teacher (50%) | 0.7759 | | Student (50%) | **0.7800** | Using only **20% labeled data**, the student model recovers **98.3%** of the fully supervised Dice score. Using **50% labeled data**, the student slightly surpasses the fully supervised baseline. --- # Ablation Study Confidence threshold analysis for pseudo-label selection: | Threshold | Accepted Masks | Dice | |-----------|---------------:|------:| | 0.60 | 1743 | 0.7506 | | 0.70 | 1685 | 0.7551 | | **0.80** | **1563** | **0.7638** | | 0.90 | 1154 | 0.7604 | The optimal threshold was **τ = 0.80**, balancing pseudo-label quality and quantity. --- # Loading a Checkpoint ```python import torch model = YourUNetModel() checkpoint = torch.load("student_50pct.pth", map_location="cpu") model.load_state_dict(checkpoint) model.eval() ``` --- # Intended Use These checkpoints are intended for: - Medical image segmentation research - Semi-supervised learning research - Benchmark comparisons - Educational purposes - Reproducibility of the accompanying paper They are **not intended for clinical diagnosis or direct patient care**. --- # Related Resources - 📄 Paper: Coming soon (SSRN / arXiv) - 💻 GitHub Repository: Coming soon - 🌐 Interactive Demo: https://huggingface.co/spaces/DevHabiba/skin-lesion-segmentation --- # License Released under the MIT License.