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相关概念视频

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

Updated: Jan 30, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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脑视:基于云的深度学习系统用于大脑图像细分,瘤检测和可视化.

Partho Ghose1, Hasan M Jamil2

  • 1Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA.

Biomedical journal
|May 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了BrainView,这是一个深度学习平台,用于使用MRI扫描来检测和分类脑瘤. 我们的模型在分类瘤类型和细分瘤方面取得了高精度,有助于早期诊断.

关键词:
大脑瘤是什么?深度学习是一种深度学习.图像的分类图像的分类.图像细分 图像细分 图像细分磁共振成像 (MRI) 是一种磁共振成像.

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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
  • 神经科学是一个神经科学.

背景情况:

  • 大脑瘤破坏神经功能,并对生命构成威胁.
  • 早期发现和分类脑瘤对于患者的治疗结果至关重要.
  • 深度学习在脑瘤图像分析方面表现有前途.

研究的目的:

  • 开发和评估一个深度学习平台,BrainView,用于脑瘤检测和细分.
  • 使用磁共振图像 (MRI) 分类脑瘤类型.
  • 通过图像细分来定位脑瘤.

主要方法:

  • 使用EfficientNetB7预训练模型进行分类 (DeepBrainNet) 和细分 (EffB7-UNet).
  • 应用深度学习技术来分析大脑MRI扫描.
  • 使用Flask和Flutter开发了一个云应用程序框架.

主要成果:

  • 在脑瘤分类中达到99.96%的准确性.
  • 在脑瘤细分方面获得了92.734%的准确性.
  • 证明了基于EfficientNetB7的模型对脑瘤分析的有效性.

结论:

  • 脑视平台在脑瘤检测和分类方面表现出高性能.
  • 深度学习模型,特别是基于EfficientNetB7的模型,对于分析大脑MRI是非常有效的.
  • 基于云的应用程序可以促进在线访问这些AI模型,用于研究和临床使用.