标题： 医学图像分割的基础AI模型 显示英文标题

标题： Foundation AI Model for Medical Image Segmentation

摘要： 基础模型是指经过大规模数据训练，并在各种任务上表现出广泛泛化能力且具有高精度的人工智能（AI）模型。这些模型提供了通用的、一通多用或多用途的解决方案，从而避免了开发针对特定任务的AI模型的需求。此类基础模型的例子包括聊天生成预训练转换器（ChatGPT）和分割任意物体模型（SAM）。这些模型已经接受了数百万到数十亿样本的训练，并在许多任务中展示了广泛的准确应用，例如文本处理（使用ChatGPT）和自然图像分割（使用SAM）。在医学图像分割——即在医学图像中寻找目标区域的任务中，存在对这种通用模型日益增长的需求。这类模型可以消除目前需要开发数千个针对特定任务的AI模型的必要性。它们还可以适应那些数据量过小而无法有效训练的任务。我们讨论了实现医学图像分割基础模型的两条途径，并评论了进展、挑战和机遇。一条途径是调整或微调原本为自然图像设计的现有模型，以便用于医学图像；另一条途径是从头开始构建模型，并专门在医学图像上进行训练。 显示英文摘要

摘要： Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or one-for-all solutions, eliminating the need for developing task-specific AI models. Examples of such foundation models include the Chat Generative Pre-trained Transformer (ChatGPT) and the Segment Anything Model (SAM). These models have been trained on millions to billions of samples and have shown wide-ranging and accurate applications in numerous tasks such as text processing (using ChatGPT) and natural image segmentation (using SAM). In medical image segmentation - finding target regions in medical images - there is a growing need for these one-for-many or one-for-all foundation models. Such models could obviate the need to develop thousands of task-specific AI models, which is currently standard practice in the field. They can also be adapted to tasks with datasets too small for effective training. We discuss two paths to achieve foundation models for medical image segmentation and comment on progress, challenges, and opportunities. One path is to adapt or fine-tune existing models, originally developed for natural images, for use with medical images. The second path entails building models from scratch, exclusively training on medical images.