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相关概念视频

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Cerebral Hemispheres01:05

Cerebral Hemispheres

The human brain, a complex organ, is functionally divided into two cerebral hemispheres—left and right. These hemispheres are interconnected by a structure of paramount importance, the corpus callosum. This substantial bundle of neural fibers is not just a bridge between the hemispheres but a crucial element for the brain's comprehensive functioning. It enables efficient communication between the two hemispheres, allowing each side of the brain to control and receive sensory and motor...
Lateralization01:28

Lateralization

Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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相关实验视频

Updated: Jun 18, 2026

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa
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Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Published on: February 2, 2018

使用DE-SAMNet模型对多类肺癌分类进行Grad-CAM增强可解释的深度学习.

Murat Kılıç1, Merve Bıyıklı1, Abdulkadir Yelman2

  • 1Turgut Ozal Medical Center, Department of Thoracic Surgery, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.

Diagnostics (Basel, Switzerland)
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

这项研究介绍了DE-SAMNet,这是一种用于从CT扫描中准确分类肺癌的深度学习模型. 可解释的AI框架提高了诊断可靠性,并支持早期检测.

关键词:
这是分类分类的分类.密集的网络121有效的网络b0肺癌是一种肺癌.空间注意模块的空间注意模块

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Scanning Dos and Don'ts: Using Magnetic Resonance Imaging in Awake Children Aged 3 to 5 Years to Assess Brain Structure and Function
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Scanning Dos and Don'ts: Using Magnetic Resonance Imaging in Awake Children Aged 3 to 5 Years to Assess Brain Structure and Function

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
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Scanning Dos and Don'ts: Using Magnetic Resonance Imaging in Awake Children Aged 3 to 5 Years to Assess Brain Structure and Function
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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
  • 在瘤学瘤学.

背景情况:

  • 肺癌 (LC) 是全球癌症死亡的主要原因.
  • 对于LC诊断的胸部CT扫描的手动解释是耗时且变化的.
  • 需要自动诊断工具来提高准确性和效率.

研究的目的:

  • 开发和评估DE-SAMNet,这是一个混合深度学习框架,用于自动化CT扫描的多类肺癌分类.
  • 评估模型在公共和私人临床数据集上的表现.
  • 使用可解释AI (XAI) 提高自动化分类的可解释性.

主要方法:

  • DE-SAMNet集成了DenseNet121和EfficientNetB0以进行多级特征提取.
  • 空间注意模块 (SAM) 通过专注于临床相关区域来完善特征表示.
  • 一个紧的聚变机制结合了最终分类的特征.

主要成果:

  • 该模型在公共数据集 (99.54%准确度) 和私人数据集 (95.96%准确度) 上实现了高性能.
  • 在肺癌分类方面,DE-SAMNet的表现优于现有的方法.
  • XAI技术 (Grad-CAM) 可视化了决策,突出了损伤特定区域的透明度.

结论:

  • DE-SAMNet为自动肺癌检测提供了一个高度准确和可解释的解决方案.
  • 可解释性特征增强了信任,并证明了早期诊断的临床潜力.
  • 该框架解决了手动CT扫描解释方面的挑战,提高了诊断的一致性.