Reza Basiri

Research

Conference paper, September 2023

Synthesizing Diabetic Foot Ulcer Images with Diffusion Model

Reza Basiri, Karim Manji, Francois Harton, Alisha Poonja, Milos R. Popovic, Shehroz S. Khan

ECML PKDD 2023 workshops (ARIAL), Communications in Computer and Information Science (Springer), 353-360. DOI 10.1007/978-3-031-74640-6_28

In brief

Medical AI is starved of data, and wound images are especially scarce. A diffusion model trained on 2,000 diabetic foot ulcer photos produced synthetic wounds realistic enough that clinicians took them for real 70% of the time. The study also shows that the standard image-quality scores, FID and KID, do not agree with clinicians, so better ways to judge synthetic medical images are needed.

Key findings

  • Clinicians labelled synthetic ulcers as real 70% of the time
  • Clinicians were still more unanimous about real images than synthetic ones
  • Presented as an invited talk at ECML PKDD 2023 in Turin, Italy

Read at the publisherarXiv preprint

Related work

  1. Multimodal Artificial Intelligence System to Enhance the Classification and Analysis of Diabetic Foot Ulcer Healing Conditions

    Reza Basiri

    PhD thesis. University of Toronto, 2025 Open access

    The doctoral work that ties the research programme together. It builds the Zivot dataset, ranks the clinical signs that matter most for healing, measures how colour, thermal and depth imaging complement each other, and introduces an attention-based network that fuses all of them to classify a wound's healing phase.

    SummaryFull text

  2. Applications of Generative AI in Diabetic Foot Ulcer Treatment

    Reza Basiri

    Book chapter. IntechOpen, 2025 Open access

    A single-author review of how generative AI is changing diabetic foot ulcer care: synthetic images where real data is scarce, automated clinical documentation, treatment planning and healing forecasts. It also covers what clinical validation and the new regulatory guidance for AI-enabled devices mean for getting these tools into practice.

    SummaryPublisher

  3. Enhancing Diabetic Foot Ulcer Assessment Through Fine-Tuned Vision-Language Models

    Reza Basiri, Asad Ghaffar, Donya Ghiasi, Meaka Tesfaye Mekonnen, Milos R. Popovic, Shehroz S. Khan

    Conference paper. IJCAI 2025 workshops, Communications in Computer and Information Science (Springer), 117-131, 2025

    General-purpose vision-language models can describe a photograph, but not the way a wound-care clinician would. This work fine-tunes vision-language models on diabetic foot ulcer images so they assess and describe wounds in clinical terms, moving from the off-the-shelf approach of UlcerGPT to models adapted to the domain.

    SummaryPublisher