Authors: Yasamin Medghalchi, Niloufar Zakariaei, Arman Rahmim, Ilker Hacihaliloglu

Published on: March 25, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.16335

Summary

• What is new: Introduction of MEDDAP pipeline leveraging Stable Diffusion models for generating informative labeled samples in medical imaging, and USLoRA for efficient fine-tuning.
• Why this is important: The lack of abundant, accurately annotated data for training deep neural networks, especially in medical cases with ultrasound images that vary widely.
• What the research proposes: MEDDAP pipeline with Stable Diffusion models enhanced by USLoRA, a fine-tuning method requiring significantly fewer resources.
• Results: Outperformed classifiers trained on original datasets and demonstrated superior performance on unseen datasets.

Technical Details

Technological frameworks used: Stable Diffusion (SD), MEDDAP, USLoRA

Models used: Deep Neural Networks, specifically for ultrasound image classification

Data used: Original small datasets in medical imaging, particularly ultrasound

Potential Impact

Healthcare providers, medical imaging companies, AI development companies focusing on healthcare applications

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