medical image segmentation using deep learning: a survey

Q: "医学图像分割使用深度学习：一项调查"。

A: 这是一篇介绍如何使用深度学习进行医学图像分割的调查文章。它讨论了医学图像分割的挑战和深度学习的优点，同时回顾了最近使用深度学习进行医学图像分割的研究成果。

相关问题

What are the specific steps of image segmentation by deep learning

由于深度学习的图像分割技术的复杂性，它的具体步骤包括：（1）数据准备，包括训练数据和测试数据的准备；（2）模型训练，通常采用深度学习的模型，如残差网络，卷积神经网络等；（3）图像调整，如改变图像的大小，颜色，亮度等；（4）测试，使用测试数据来评估模型的准确度。

Transformer-Based Visual Segmentation: A Survey

Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.

This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.

The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.

Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.