专为医学3D建模设计的生成模型，提升精度与效率。
Shap-MeD
- 基于Shap-e微调，专用于生物医学对象生成
- 潜空间生成误差降至0.089，优于原始模型的0.147
- 生成结果结构更准确，适合医疗仿真与个性化设计
我们提出Shap-MeD，一个专注于生物医学领域的文本到3D物体生成模型。该研究旨在开发辅助工具，加速医学3D建模进程。医学3D建模广泛应用于手术模拟与规划、个性化假体设计、医学教育、解剖模型制作及研究原型开发。为此，我们采用OpenAI开源的Shap-e模型，并使用生物医学对象数据集进行微调。在评估集上，模型潜空间生成的均方误差（MSE）为0.089，低于Shap-e的0.147。此外，我们进行了定性评估，对比了多种模型在生物医学对象生成上的表现，结果显示Shap-MeD具有更高的结构准确性。
原文摘要 · Abstract (English)
We present Shap-MeD, a text-to-3D object generative model specialized in the biomedical domain. The objective of this study is to develop an assistant that facilitates the 3D modeling of medical objects, thereby reducing development time. 3D modeling in medicine has various applications, including surgical procedure simulation and planning, the design of personalized prosthetic implants, medical education, the creation of anatomical models, and the development of research prototypes. To achieve this, we leverage Shap-e, an open-source text-to-3D generative model developed by OpenAI, and fine-tune it using a dataset of biomedical objects. Our model achieved a mean squared error (MSE) of 0.089 in latent generation on the evaluation set, compared to Shap-e's MSE of 0.147. Additionally, we conducted a qualitative evaluation, comparing our model with others in the generation of biomedical objects. Our results indicate that Shap-MeD demonstrates higher structural accuracy in biomedical object generation.
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