Although deep learning models have become the main method for medical image
segmentation, they often cannot be extended to unknown segmentation tasks
involving new anatomical structures, image shapes, or labels. For new
segmentation tasks, researchers often have to retrain or fine-tune the model,
which is time-consuming and poses a significant obstacle to clinical
researchers, who often lack the resources and professional knowledge to train
neural networks. Therefore, we proposed a general method that can solve unknown
medical image segmentation tasks without requiring additional training. Given
an example set of images and prompts for defining new segmentation tasks,
GMISeg applies a novel low-rank fine-tuning strategy based on the proposed
approach to the SAM (Segment Anything Model) image encoder, and works with the
prompt encoder and mask decoder to fine-tune the labeled dataset without the
need for additional training. To achieve generalization of new tasks, we used
medical image datasets with different imaging modes for different parts. We
trained and generalized GMISeg on a different set of anatomical and imaging
modes using cardiac images on other site datasets. We have demonstrated that
GMISeg outperforms the latest methods on unknown tasks and have conducted a
comprehensive analysis and summary of the important performance of the proposed
method.

提出了一种无需额外训练即可解决未知医学图像分割任务的通用方法，该方法通过使用基于所提出方法的 SAM（Segment Anything Model）图像编码器的一种新型低秩微调策略，与提示编码器和掩膜解码器一起微调标记数据集，实现新任务的泛化。该方法在不同部位的医学图像数据集上进行训练和泛化，并在其他地点数据集上使用心脏图像进行了验证，证明其在未知任务上的优越性能，并对所提方法的重要性能进行了全面分析和总结。

GMISeg: 无需重新训练的通用医学图像分割

GMISeg: General Medical Image Segmentation without Re-Training

Our paper presents My3DGen, a practical system for creating a personalized
and lightweight 3D generative prior using as few as 10 images. My3DGen can
reconstruct multi-view consistent images from an input test image, and generate
novel appearances by interpolating between any two images of the same
individual. While recent studies have demonstrated the effectiveness of
personalized generative priors in producing high-quality 2D portrait
reconstructions and syntheses, to the best of our knowledge, we are the first
to develop a personalized 3D generative prior. Instead of fine-tuning a large
pre-trained generative model with millions of parameters to achieve
personalization, we propose a parameter-efficient approach. Our method involves
utilizing a pre-trained model with fixed weights as a generic prior, while
training a separate personalized prior through low-rank decomposition of the
weights in each convolution and fully connected layer. However,
parameter-efficient few-shot fine-tuning on its own often leads to overfitting.
To address this, we introduce a regularization technique based on symmetry of
human faces. This regularization enforces that novel view renderings of a
training sample, rendered from symmetric poses, exhibit the same identity. By
incorporating this symmetry prior, we enhance the quality of reconstruction and
synthesis, particularly for non-frontal (profile) faces. Our final system
combines low-rank fine-tuning with symmetry regularization and significantly
surpasses the performance of pre-trained models, e.g. EG3D. It introduces only
approximately 0.6 million additional parameters per identity compared to 31
million for full finetuning of the original model. As a result, our system
achieves a 50-fold reduction in model size without sacrificing the quality of
the generated 3D faces. Code will be available at our project page:
this https URL

My3DGen 是一个实用系统，使用不超过 10 张图像创建个性化和轻量级的 3D 生成先验系统。该系统利用预训练模型的固定权重作为通用先验，通过低秩分解在每个卷积和完全连接层的权重上进行单独的个性化先验训练，并通过基于人脸对称的规则化技术调整网络，指导其学习参数并减少过拟合。该系统仅增加了与完全微调的原始模型相比，每个身份约 0.6 百万个附加参数，同时保持生成的 3D 人脸的质量。