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関連する概念動画

Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Classifying Matter by State02:49

Classifying Matter by State

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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The Thyroid Gland01:23

The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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超音波特徴量のデジタル化による甲状腺結節の分類における解釈可能な機械学習モデル

Zhuyao Li1, Yu Yan2, Xiang Li1

  • 1Department of Surgery, The First Affiliated Hospital of Zhengzhou University, No. 1 East Jianshe Road, Zhengzhou 450000, China.

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まとめ
この要約は機械生成です。

UltraMCは、デジタル化された超音波特徴量を用いて、従来型および複雑なミイラ化甲状腺結節を正確に分類する、解釈可能な新しい機械学習モデルである。このホワイトボックスフレームワークは、甲状腺結節分類の診断精度を向上させる。

キーワード:
甲状腺結節機械学習超音波画像診断人工知能医療画像処理

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科学分野:

  • 放射線医学
  • 人工知能
  • 医用画像処理

背景:

  • 甲状腺結節は一般的であり、適切な管理のためには正確な分類が必要である。
  • 良性および悪性の甲状腺結節、特に複雑な症例を区別することは、臨床的な課題として残っている。
  • 現在の診断方法は、精度の向上のために高度な計算アプローチから恩恵を受ける可能性がある。

研究 の 目的:

  • 甲状腺結節のためのデジタル化された解釈可能な機械学習分類モデルを開発する。
  • 複雑な甲状腺結節を正確に認識し、従来型の結節を効率的に診断する。
  • 分類向上のために、デジタル化された超音波特徴量をホワイトボックスフレームワークに統合する。

主な方法:

  • 7つの中国の医療センター（2011-2021）からの甲状腺超音波画像のレトロスペクティブな収集。
  • フロントエンドネットワークとバックエンドネットワークを備えた2層解釈可能分類モデルUltraMCの開発。
  • 精度、感度、特異度、ROC曲線を用いたUltraMCの評価。

主要な成果:

  • データセットは73,826人の患者で構成され、フロントエンドネットワークは従来型結節に対して92.9%の精度を達成した。
  • バックエンドネットワークは、ミイラ化甲状腺結節（MTN）に対して88.5%の精度を達成した。
  • MTN分類に対するUltraMCの全体的な診断精度は91.8%であり、高いAUC値を示した。

結論:

  • 2層解釈可能分類モデル（UltraMC）は、従来型およびミイラ化甲状腺結節の両方に対して高い診断精度を示す。
  • ホワイトボックスフレームワーク内のデジタル化された超音波特徴量は、複雑な甲状腺結節の分類を効果的にサポートする。
  • このアプローチは、甲状腺結節評価における診断能力を向上させるための有望なツールを提供する。