Shap-MeD
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913747030769664 |
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| author | Laverde, Nicolás Robles, Melissa Rodríguez, Johan |
| author_facet | Laverde, Nicolás Robles, Melissa Rodríguez, Johan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shap-MeD Laverde, Nicolás Robles, Melissa Rodríguez, Johan Graphics Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition 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. |
| title | Shap-MeD |
| topic | Graphics Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15562 |