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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

453
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
453
Classification of Systems-I01:26

Classification of Systems-I

544
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
544
Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Survival Tree01:19

Survival Tree

382
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

Updated: Jan 13, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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深度合奏学习和可解释的AI用于地球星真菌物种的多类分类.

Eda Kumru1, Aras Fahrettin Korkmaz2, Fatih Ekinci3

  • 1Graduate School of Natural and Applied Sciences, Ankara University, 06830 Ankara, Türkiye.

Biology
|October 29, 2025
PubMed
概括

这项研究使用深度学习和可解释的人工智能准确分类八种类似的地球星真菌物种. EfficientNet-B3 + DeiT模型实现了高精度和可解释性,显示了农业监测的潜力.

关键词:
深度学习是一种深度学习.地球星的真菌 地球星的真菌组合模型组合模型组合模型可以解释的AIAI菌类的分类是真菌的分类.形态上的相似性.

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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科学领域:

  • 菌类学和计算生物学
  • 人工智能在分类学中的应用

背景情况:

  • 宏观的地球星真菌 (属Astraeus和Geastrum) 呈现出显著的形态相似性,使视觉识别具有挑战性.
  • 这些物种的准确分类对于生态研究和了解真菌生物多样性至关重要.

研究的目的:

  • 开发和评估深度学习模型,用于对八种形态相似的地球星真菌物种进行基于图像的多类分类.
  • 通过可解释的人工智能 (XAI) 技术提高模型的解释性.
  • 引入新的混合组合模型,以提高分类稳定性和准确性.

主要方法:

  • 使用了八种深度学习架构 (CNN和变压器),包括EfficientNetV2-M,DenseNet121,MaxViT-S,DeiT,RegNetY-8GF,MobileNetV3,EfficientNet-B3和MnasNet. 这三种架构都在使用中.
  • 使用Grad-CAM和Score-CAM来可视化分类决策的逻辑.
  • 设计和评估了两种混合组合模型:EfficientNet-B3 + DeiT和DenseNet121 + MaxViT-S. 这两种组合模型的设计和评估.

主要成果:

  • 单个模型的准确性在86.16%至96.23%之间,其中EfficientNet-B3表现最好.
  • 组合模型实现了更好的稳定性,EfficientNet-B3 + DeiT达到93.71%的准确性,DenseNet121 + MaxViT-S达到93.08%的准确性.
  • EfficientNet-B3 + DeiT组合表现出最平衡的性能,精度为93.83%,回忆率为93.72%,MCC为0.9282.
  • XAI技术为分类决策提供了生物学上有意义的见解.

结论:

  • 拟议的深度学习和XAI框架成功地以高准确性和可解释性对形态上相似的地球星真菌进行了分类.
  • 混合组合模型,特别是EfficientNet-B3 + DeiT,提供强大而稳定的分类性能.
  • 这种方法在监测农业生态系统中的共生真菌和促进可持续实践方面具有潜在的应用.