skin-lesion-segmentation-unet

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.

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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.

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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

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Available Checkpoints

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Performance

Fully Supervised Baseline

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Semi-Supervised Results

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.

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Ablation Study

Confidence threshold analysis for pseudo-label selection:

The optimal threshold was τ = 0.80, balancing pseudo-label quality and quantity.

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Loading a Checkpoint

import torch

model = YourUNetModel()

checkpoint = torch.load("student_50pct.pth", map_location="cpu")

model.load_state_dict(checkpoint)

model.eval()

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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 (Zenodo): https://doi.org/10.5281/zenodo.21320667
• 💻 GitHub Repository: https://github.com/HabibaSajid321/semi-supervised-skin-lesion-segmentation
• 🤗 Model Checkpoints: https://huggingface.co/DevHabiba/skin-lesion-segmentation-unet
• 🌐 Interactive Demo: https://huggingface.co/spaces/DevHabiba/skin-lesion-segmentation

Citation

If you use this work in your research, please cite:

@misc{sajid2026teacher,
  author       = {Habiba Sajid},
  title        = {Teacher--Student Pseudo-Labeling for Semi-Supervised Skin Lesion Segmentation: A Reproducible Baseline},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21320667},
  url          = {https://doi.org/10.5281/zenodo.21320667}
}

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# License

Released under the MIT License.