This repository contains the disease-conditioned Textual Inversion adapter weights for syringocystadenoma_papilliferum, trained on top of stabilityai/stable-diffusion-2-1-base.

It was introduced in the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification as part of the cgDDI (Controllable Generation of Diverse Dermatological Imagery) framework.

The cgDDI framework synthesizes diverse, skin-tone-balanced, and disease-conditioned dermatological imagery to support fair and efficient medical image classification.

Code: ControllableGenDDI GitHub Repository
Dataset: hcarrion/ControllableGenDDI Dataset

Citation

bibtex

@inproceedings{carrion2026cgddi,
  title     = {Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification},
  author    = {Carri{\`o}n, H{\`e}ctor and Norouzi, Narges},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2026},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science}
}

hcarrion/syringocystadenoma_papilliferum

cgDDI: syringocystadenoma_papilliferum Textual Inversion

Citation

hcarrion/
syringocystadenoma_papilliferum