Reply to comment on "Feasibility of improving vocal fold pathology image classification with synthetic images generated by DDPM-based GenAI: a pilot study", by Daungsupawong and Wiwanitkit
Abstract
In response to the recent commentary on our pilot study using denoising diffusion probabilistic models (DDPMs) for vocal fold structural pathology image augmentation, we clarify our study's rationale and methods. The original work evaluated the feasibility of synthetic image generation to address data scarcity and imbalance. We affirm that several concerns raised were already addressed and note that statistical testing was not conducted due to the study's exploratory nature. Additional clarifications are provided in this letter to address points raised in the commentary and to outline directions for future research.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.