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相关概念视频

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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相关实验视频

Updated: Jun 24, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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实施视觉转换器用于分类二维生物医学图像.

Arindam Halder1, Sanghita Gharami1, Priyangshu Sadhu1

  • 1Department of Information Technology, Jadavpur University, Jadavpur University Salt Lake Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Kolkata, West Bengal, 700106, India.

Scientific reports
|May 31, 2024
PubMed
概括
此摘要是机器生成的。

视觉变压器 (ViT) 模型在医疗图像分类任务中表现出色. 这项研究在BloodMNIST,BreastMNIST,PathMNIST和RetinaMNIST数据集上取得了新的基准,证明了ViT的存在.

关键词:
生物医学图像分类的分类.血液部长 (BloodMNIST) 是一个血液部长.乳腺MNIST 乳腺部长深度学习是一种深度学习.在MedMNISTv2上播放这就是PathMNIST.视网膜MNIST是一个人.视觉变压器 视觉变压器

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 医学成像数据的快速增长需要先进的机器学习算法用于医疗保健应用.
  • 准确的生物医学图像分类对于疾病诊断和治疗规划至关重要.
  • MedMNISTv2数据集为评估二维医学图像分类模型提供了多样化的基准.

研究的目的:

  • 分析视觉变压器 (ViT) 模型在MedMNISTv2数据集中的多种医学成像模式上的效率.
  • 评估ViT在捕获复杂模式的能力,用于医学图像分类.
  • 使用ViT.使用BloodMNIST,BreastMNIST,PathMNIST和RetinaMNIST数据集建立新的基准准度.

主要方法:

  • 从MedMNISTv2中选择了四个子集:BloodMNIST,BreastMNIST,PathMNIST和RetinaMNIST,这些子集因其不同的模式和样本大小而被选择.
  • 预处理的输入图像用于模型训练.
  • 在选定的数据集上训练了ViT-base-patch16-224模型,并使用关键指标评估了性能.

主要成果:

  • 实现了新的基准准确率:BloodMNIST的97.90%,乳腺MNIST的90.38%,PathMNIST的94.62%,以及视网膜MNIST的57%.
  • 展示了视觉变压器模型在分类各种医学成像数据中的有效性.
  • 该模型在很大程度上超越了现有的基准指标.

结论:

  • 视觉变压器模型对医学图像分析和分类有很大的前景.
  • 这些发现支持在医疗保健中采用ViT模型,以提高诊断准确度.
  • 进一步探索ViT模型可以帮助医疗专业人员在临床决策中.