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Found 4 papers, 1 papers with code
MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection
We perform our experiments for the specific task of medical image generation and outperform existing state-of-the-art training-time mitigation strategies by fine-tuning as few as 0. 019% of model parameters.
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets.
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse.
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