cgDDI: fibrous_papule

These are the textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base to generate images of fibrous papule.

This model was introduced as part of the paper: Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification (MICCAI 2026).

• Code: hectorcarrion/ControllableGenDDI
• Dataset: hcarrion/ControllableGenDDI

Methodology

The cgDDI framework learns disease-specific concept tokens via textual inversion and fine-tunes latent diffusion models to generate realistic, skin-tone-balanced synthetic imagery. These synthetic images are used to train fairer and more robust malignancy classifiers.

Citation

@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}
}