Bora: Biomedical Generalist Video Generation Model
- URL: http://arxiv.org/abs/2407.08944v2
- Date: Tue, 16 Jul 2024 03:00:07 GMT
- Title: Bora: Biomedical Generalist Video Generation Model
- Authors: Weixiang Sun, Xiaocao You, Ruizhe Zheng, Zhengqing Yuan, Xiang Li, Lifang He, Quanzheng Li, Lichao Sun,
- Abstract summary: This paper introduces Bora, first model designed for text-guided biomedical video generation.
It is fine-tuned through model alignment and instruction tuning using a newly established medical video corpus.
Bora is capable of generating high-quality video data across four distinct biomedical domains.
- Score: 20.572771714879856
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for medical AI development. Diffusion models can now generate realistic images from text prompts, while recent advancements have demonstrated their ability to create diverse, high-quality videos. However, these models often struggle with generating accurate representations of medical procedures and detailed anatomical structures. This paper introduces Bora, the first spatio-temporal diffusion probabilistic model designed for text-guided biomedical video generation. Bora leverages Transformer architecture and is pre-trained on general-purpose video generation tasks. It is fine-tuned through model alignment and instruction tuning using a newly established medical video corpus, which includes paired text-video data from various biomedical fields. To the best of our knowledge, this is the first attempt to establish such a comprehensive annotated biomedical video dataset. Bora is capable of generating high-quality video data across four distinct biomedical domains, adhering to medical expert standards and demonstrating consistency and diversity. This generalist video generative model holds significant potential for enhancing medical consultation and decision-making, particularly in resource-limited settings. Additionally, Bora could pave the way for immersive medical training and procedure planning. Extensive experiments on distinct medical modalities such as endoscopy, ultrasound, MRI, and cell tracking validate the effectiveness of our model in understanding biomedical instructions and its superior performance across subjects compared to state-of-the-art generation models.
Related papers
- SurGen: Text-Guided Diffusion Model for Surgical Video Generation [0.6551407780976953]
SurGen is a text-guided diffusion model tailored for surgical video synthesis.
We validate the visual and temporal quality of the outputs using standard image and video generation metrics.
Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.
arXiv Detail & Related papers (2024-08-26T05:38:27Z) - Endora: Video Generation Models as Endoscopy Simulators [53.72175969751398]
This paper introduces model, an innovative approach to generate medical videos that simulate clinical endoscopy scenes.
We also pioneer the first public benchmark for endoscopy simulation with video generation models.
Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research.
arXiv Detail & Related papers (2024-03-17T00:51:59Z) - BiomedJourney: Counterfactual Biomedical Image Generation by
Instruction-Learning from Multimodal Patient Journeys [99.7082441544384]
We present BiomedJourney, a novel method for counterfactual biomedical image generation by instruction-learning.
We use GPT-4 to process the corresponding imaging reports and generate a natural language description of disease progression.
The resulting triples are then used to train a latent diffusion model for counterfactual biomedical image generation.
arXiv Detail & Related papers (2023-10-16T18:59:31Z) - Towards Generalist Biomedical AI [28.68106423175678]
We introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system.
Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data.
We conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales.
arXiv Detail & Related papers (2023-07-26T17:52:22Z) - XrayGPT: Chest Radiographs Summarization using Medical Vision-Language
Models [60.437091462613544]
We introduce XrayGPT, a novel conversational medical vision-language model.
It can analyze and answer open-ended questions about chest radiographs.
We generate 217k interactive and high-quality summaries from free-text radiology reports.
arXiv Detail & Related papers (2023-06-13T17:59:59Z) - LLaVA-Med: Training a Large Language-and-Vision Assistant for
Biomedicine in One Day [85.19963303642427]
We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.
The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics.
This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
arXiv Detail & Related papers (2023-06-01T16:50:07Z) - BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types.
Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
arXiv Detail & Related papers (2023-05-26T17:14:43Z) - BiomedCLIP: a multimodal biomedical foundation model pretrained from
fifteen million scientific image-text pairs [48.376109878173956]
We present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets.
PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles.
Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing.
arXiv Detail & Related papers (2023-03-02T02:20:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.