doronys/synthmed-retina-hpp-sample

SynthMed Retina — HPP I2I Sample

A sample of 2,500 synthetic retinal fundus images generated via image-to-image (I2I) diffusion from the Human Phenotype Project (10K) cohort, paired with anonymized patient metadata.

Images were produced with the hpp-retina LoRA adapter at a fixed I2I strength of 0.5, which trades off structural fidelity against re-identification risk (see the paper for the full privacy–utility curve).

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Columns

Column	Type	Description
image	Image	512 × 512 RGB retinal fundus image
name	str	Unique image identifier
strength	float	I2I diffusion strength used for generation (always 0.5)
prompt	str	Text prompt passed to the diffusion model
participant_id	int	Anonymized person ID — consistent across rows for the same participant
eye	str	Laterality — "od" (right) or "os" (left)
age	float	Age in years
gender	float	0 = female, 1 = male
bt__mcv	float	Mean corpuscular volume (fL)
bt__wbc	float	White blood cell count (10³/µL)
bt__platelets	float	Platelet count (10³/µL)
bt__hemoglobin	float	Hemoglobin (g/dL)
bt__creatinine	float|null	Creatinine (mg/dL)
bt__glucose	float|null	Fasting glucose (mg/dL)
bt__total_cholesterol	float|null	Total cholesterol (mg/dL)
bt__hdl_cholesterol	float|null	HDL cholesterol (mg/dL)
bt__non_hdl_cholesterol	float|null	Non-HDL cholesterol (mg/dL)
bt__triglycerides	float|null	Triglycerides (mg/dL)
bt__hba1c	float|null	HbA1c (%)
bt__ferritin	float|null	Ferritin (ng/mL)
bt__albumin	float|null	Albumin (g/dL)
sitting_blood_pressure_pulse_rate	float	Pulse rate (bpm)
sitting_blood_pressure_systolic	float	Systolic blood pressure (mmHg)
bmi	float	Body mass index (kg/m²)
hips	float	Hip circumference (cm)
waist	float	Waist circumference (cm)
hand_grip_right	float	Right-hand grip strength (kg)
*_norm / *_cat	float	Min-max normalized counterpart (-1.0 = missing)

Note on missing values: raw label columns use null; the normalized *_norm columns use -1.0 as a sentinel for missing values.

Note on gender encoding: gender=0 → female, gender=1 → male (consistent with gender_cat class index 0 = female, 1 = male).

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Generation Details

Parameter	Value
Base model	CompVis/stable-diffusion-v1-4
LoRA adapter	doronys/synthmed-loras — hpp-retina/lora_weights.safetensors
LoRA rank / alpha	64 / 32
Scheduler	DDIM
Inference steps	50
I2I strength	0.5
Resolution	512 × 512
Sample size	2,500 (one synthetic image per source image)

Source images are retinal fundus photographs from the HPP 10K cohort. Each source image was processed once (unique source → unique synthetic image).

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Quick Start

from datasets import load_dataset

ds = load_dataset("doronys/synthmed-retina-hpp-sample", split="train")

# Each example has an 'image' (PIL.Image) plus all metadata columns
example = ds[0]
print(example["prompt"])   # "retina fundoscopy right eye dilated age=47 gender=female bp systolic=105"
print(example["age"])      # 47.0
example["image"].show()

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About

These images were produced as part of a study evaluating I2I diffusion as a practical approach to medical image de-identification. Key findings:

• I2I outperforms T2I on pixel/perceptual fidelity and biomarker agreement at all conditioning strengths.
• At strength = 0.5: top-1 re-identification rate drops to ~20% while biomarker agreement (hemoglobin, Pearson r) remains ~0.7.
• Cross-cohort transfer: classifiers pretrained on I2I synthetic retinal images perform comparably to real-image pretraining when fine-tuning on UK Biobank data.

See the model card for the full privacy–utility curve and generation instructions.

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Citation

@article{synthmed2026,
  title   = {Privacy-Preserving Synthetic Medical Images via Image Conditioned Diffusion Models},
  author  = {Yaya-Stupp, Doron and Lutsker, Guy and Spiegel, Or and Segal, Eran},
  year    = {2026},
}

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License

CC BY 4.0 — free to use, share, and adapt with attribution.

The underlying source images are from the Human Phenotype Project 10K cohort and are not included in this repository. Only the synthetic outputs and anonymized metadata are released here.