DINOv3-ISIC2018 Segmentation

Transfer Learning vs Training from Scratch for Medical Image Segmentation

An empirical study comparing frozen DINOv3 encoders against baseline U-Net for skin lesion segmentation on the ISIC2018 dataset across different data regimes (25%, 50%, 100%).

Key Finding: Transfer learning dominates with limited data (+4.8% at 25%), but baseline U-Net surpasses frozen encoders with full data (+1.3% at 100%).

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Table of Contents

• Overview
• Key Results
• Installation
• Quick Start
• Project Structure
• Experiments
• Visualizations
• Citation
• License

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Overview

This repository contains the complete implementation and analysis of an independent research project comparing transfer learning (frozen DINOv3 encoders) against training from scratch (baseline U-Net) for medical image segmentation.

Research Questions:

1. Does transfer learning beat baseline in low-data scenarios?
2. Does baseline catch up with full data?
3. Is larger encoder always better (Small < Base < Large)?

Dataset: ISIC2018 Skin Lesion Analysis Challenge (2,594 training images)

Models Compared:

• Baseline U-Net (7.76M params, trained from scratch)
• DINOv3-Small + Custom Decoder (25M total, 4M trainable)
• DINOv3-Base + Custom Decoder (90M total, 4M trainable)
• DINOv3-Large + Custom Decoder (156M total, 4M trainable)

Motivation: As a recent graduate in Signal and Image Processing, I wanted to test some intuitions about foundation models and the self-supervised learning paradigm on a concrete use case. This project explores when pre-trained models (like DINOv3) actually provide value versus simpler approaches trained from scratch - particularly in data-constrained medical imaging scenarios.

Context: This work was conducted independently during my job search period to deepen my understanding of transfer learning trade-offs and provide practitioners with evidence-based guidance on model selection based on dataset size.

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Key Results

Performance Summary

Model	25% Data	50% Data	100% Data
Baseline U-Net	0.828	0.867	0.898 ⭐
DINOv3-Small	0.867	0.887	0.893
DINOv3-Base	0.876	0.897	0.885
DINOv3-Large	0.878	0.877	0.894

Key Findings

H1: Transfer Learning Dominates Low-Data Scenarios ✅

• +4.8% advantage at 25% data (650 images)
• 7× more data-efficient than training from scratch
• $12-14K saved in annotation costs

H2: Baseline Surpasses at Scale ✅

• Baseline wins at 100% data (+1.3% over DINOv3-Base)
• Complete reversal from low-data regime
• Win rate: 43.5% → 62.5%

H3: Size Hierarchy Doesn't Hold ❌

• No consistent Small < Base < Large hierarchy
• DINOv3-Base peaks at 50%, then declines
• Optimal model depends on data regime

Practical Implications

< 1000 images: Use DINOv3 (frozen)

• ROI: 10-20× on annotation costs
• Better robustness on hard cases

> 2000 images: Use Baseline U-Net

• Simpler, faster, better performance
• Lower computational requirements

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Installation

Requirements

• Python 3.11+
• PyTorch 2.0+

Setup

# Clone the repository
git clone https://github.com/getrichthroughcode/dinov3-isic2018-segmentation.git
cd dinov3-isic2018-segmentation

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install package in development mode
pip install -e .

Dataset Preparation

1. Download ISIC2018 dataset from official source

2. The dataset is managed automatically using the any-gold library:

   • Images are cached locally for efficient access
   • No manual organization required
   • Data splits handled by the library

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Quick Start

Training

Train Baseline U-Net (100% data):

python scripts/train.py \
    --model baseline \
    --data-fraction 1.0 \
    --epochs 50 \
    --batch-size 8 \
    --lr 3e-4 \
    --output-dir runs/baseline_100_percent

Train DINOv3-Base (50% data):

python scripts/train.py \
    --model dinov3_base \
    --data-fraction 0.5 \
    --epochs 50 \
    --batch-size 8 \
    --lr 3e-4 \
    --output-dir runs/dinov3b_unet_50_percent

Evaluation

python scripts/evaluate.py \
    --model-path runs/baseline_100_percent/best.pt \
    --data-split test \
    --output-dir results/

Visualization

python scripts/visualize_samples.py \
    --model-path runs/baseline_100_percent/best.pt \
    --num-samples 10 \
    --output-dir visualizations/

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Project Structure

dinov3-isic2018-segmentation/
│
├── src/dinoseg/              # Main package
│   ├── models/
│   │   ├── baseline_unet.py      # U-Net implementation
│   │   └── dino_v3_unet.py       # DINOv3-UNet architecture
│   ├── training/
│   │   └── trainer.py            # Training loop
│   ├── data/
│   │   └── loader.py             # Data loading with any-gold
│   └── utils/
│       ├── metrics.py            # Dice, HD95 metrics
│       ├── viz.py                # Visualization utilities
│       └── seed.py               # Reproducibility
│
├── scripts/                  # Executable scripts
│   ├── train.py                  # Training script
│   ├── evaluate.py               # Evaluation script
│   └── visualize_samples.py      # Visualization
│
├── assets/                   # Result visualizations
│   ├── full_data/                # 100% data results
│   ├── moderate_data/            # 50% data results
│   └── low_data/                 # 25% data results
│
├── tests/                    # Unit tests
│   ├── test_forward.py           # Model forward pass tests
│   └── test_metrics.py           # Metric calculation tests
│
│
├── requirements.txt          # Python dependencies
├── pyproject.toml           # Package configuration
├── Makefile                 # Common commands
├── README.md                # This file
└── LICENSE                  # MIT License

**Note**: Dataset is managed by `any-gold` library and cached locally (default: `~/.cache/isic2018/`)

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Experiments

Data Regimes Tested

• 25%: ~650 images (low-data scenario)
• 50%: ~1,300 images (medium-data scenario)
• 100%: ~2,594 images (full dataset)

Models Evaluated

All models trained with:

• Optimizer: AdamW (lr=3e-4, weight_decay=1e-4)
• Scheduler: CosineAnnealingLR
• Loss: Binary Cross-Entropy with Logits
• Batch size: 8
• Epochs: 100 (with early stopping)
• Preprocessing: Images resized to 256×256

Metrics

• Dice Coefficient:
• Intersection over Union (Jaccard):

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Visualizations

Sample visualizations from the experiments:

Distribution Comparison

25% Data	50% Data	100% Data

Probability Maps

Examples showing model calibration:

Model Agreement

Visualization of inter-model consensus:

More visualizations available in assets/ directory.

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Reproducing Results

Full Reproduction Pipeline

# 1. Install dependencies
make install

make train-all

# 3. Evaluate all models
make evaluate-all

# 4. Generate visualizations
make visualize-all

Note: The dataset is automatically downloaded and cached by any-gold during first training run.

Individual Model Training

# Train specific model at specific data fraction
make train MODEL=baseline FRACTION=1.0
make train MODEL=dinov3_base FRACTION=0.5
make train MODEL=dinov3_small FRACTION=0.25
make train MODEL=dinov3_large FRACTION=1.0

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Architecture Details

Baseline U-Net

Standard encoder-decoder architecture:

• Parameters: 7.76M (all trainable)
• Encoder: 4 levels with MaxPool downsampling
• Decoder: Transposed convolution upsampling
• Skip connections: Concatenation
• Trained from scratch on ISIC2018

DINOv3-UNet

Hybrid architecture with frozen encoder:

• Encoder: Frozen DINOv3 Vision Transformer (pre-trained on LVD-142M)
• DINO Adapter: Fuses frozen features with spatial details
• Shared Context Aggregator: Extracts global scene understanding
• FAPM: Preserves fine-grained details during feature compression
• Decoder: Standard U-Net decoder

Trainable parameters:

• Small: 4M / 25M (16%)
• Base: 4M / 90M (4%)
• Large: 4M / 152M (2%)

Architecture inspired by Dino U-Net (Gao et al., 2025), re-implemented from scratch.

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Analysis & Blog Post

Detailed analysis of results available in:

• Published Blog: [Link to Medium/Blog] (coming soon - curating it)

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Citation

If you use this code or findings in your research, please cite:

@misc{diallo2026transfer,
  title={Transfer Learning vs Training from Scratch for Medical Image Segmentation:
         An Empirical Study on ISIC2018},
  author={Diallo, Abdoulaye},
  year={2026},
  howpublished={GitHub repository},
  url={https://github.com/getrichthroughcode/dinov3-isic2018-segmentation}
}

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License

This project is licensed under the MIT License - see LICENSE file for details.

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Acknowledgments

• Dataset: ISIC2018 Skin Lesion Analysis Challenge
• Foundation Model: DINOv3 by Meta AI (Oquab et al., 2023)
• Architecture Inspiration: Dino U-Net (Gao et al., 2025)
• Data Management: any-gold library for efficient dataset handling

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Contact

Abdoulaye Diallo Signal and Image processing Engineer LinkedIn: [https://www.linkedin.com/in/abdiallo-ai]

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v1.0.0 (January 2026)

• Initial release
• Complete implementation of baseline U-Net and DINOv3-UNet variants
• Experiments on 3 data regimes (25%, 50%, 100%)
• Comprehensive analysis with 96 visualizations

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