增强犬肌肉骨骼诊断：利用合成图像数据在可视化文档上预训练AI模型
Enhancing Canine Musculoskeletal Diagnoses: Leveraging Synthetic Image Data for Pre-Training AI-Models on Visual Documentations
AI总结：
针对犬肌肉骨骼可视化诊断文档数据稀缺问题，本研究提出用合成图像数据预训练AI模型的方法，在小样本评估集上诊断准确率提升约10%，为缓解训练数据不足提供了可推广的思路。
摘要：
犬肌肉骨骼系统检查是兽医临床中的一项挑战性任务。本研究开发了一种新方法，可通过可视化表示高效记录犬的病情。但由于这种可视化文档是全新的，目前尚无现成的训练数据。因此，本研究的目标是缓解数据稀缺的影响，以开发基于AI的诊断支持系统。为此，我们探究了模拟真实疾病可视化文档的合成数据用于AI模型预训练的潜力。我们提出了一种生成模拟真实可视化文档的合成图像数据的方法。首先生成包含3个不同类别的基础数据集，随后创建包含36个不同类别的更复杂数据集。两个数据集均用于AI模型的预训练。之后，我们构建了一个评估数据集，包含针对5种不同疾病的250份人工制作的可视化文档，该数据集还包含一个有25个样本的子集。在包含25个样本的评估数据集上获得的结果表明，使用模拟真实世界可视化文档的生成合成图像时，诊断准确率显著提升了约10%。但这些结果在包含250个样本的更大评估数据集上并不成立，这表明使用合成数据预训练AI模型的优势主要体现在特定疾病的可视化文档样本量较少的场景中。总体而言，本研究为通过策略性使用生成的合成数据缓解训练数据有限带来的限制提供了宝贵见解，所提出的方法可应用于犬肌肉骨骼评估领域之外的场景。
英文摘要：
The examination of the musculoskeletal system in dogs is a challenging task in veterinary practice. In this work, a novel method has been developed that enables efficient documentation of a dog's condition through a visual representation. However, since the visual documentation is new, there is no existing training data. The objective of this work is therefore to mitigate the impact of data scarcity in order to develop an AI-based diagnostic support system. To this end, the potential of synthetic data that mimics realistic visual documentations of diseases for pre-training AI models is investigated. We propose a method for generating synthetic image data that mimics realistic visual documentations. Initially, a basic dataset containing three distinct classes is generated, followed by the creation of a more sophisticated dataset containing 36 different classes. Both datasets are used for the pre-training of an AI model. Subsequently, an evaluation dataset is created, consisting of 250 manually created visual documentations for five different diseases. This dataset, along with a subset containing 25 examples. The obtained results on the evaluation dataset containing 25 examples demonstrate a significant enhancement of approximately 10% in diagnosis accuracy when utilizing generated synthetic images that mimic real-world visual documentations. However, these results do not hold true for the larger evaluation dataset containing 250 examples, indicating that the advantages of using synthetic data for pre-training an AI model emerge primarily when dealing with few examples of visual documentations for a given disease. Overall, this work provides valuable insights into mitigating the limitations imposed by limited training data through the strategic use of generated synthetic data, presenting an approach applicable beyond the canine musculoskeletal assessment domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
