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関連する概念動画

Reliability and Validity01:29

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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The work done by an external force on a particle changes its kinetic energy. However, internal forces must also be considered for a system of interacting particles. The potential energy formulation helps formulate the effect of internal forces. The net work done by an external force can be written in terms of the total change of mechanical energy, which includes both kinetic and potential energies.
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Newton's first law states that a net external force causes a change in motion. External forces act on an object or system, originating outside of the object or system. In contrast, internal forces originate inside the system of interest and do not lead to any acceleration. In simpler words, internal forces are forces that act on one part of an object and are exerted by another part of the same object. External forces are forces that act on an object due to some other object. Therefore, when...
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External respiration occurs in the lungs, and it is the first step in the journey of oxygen inside the body. When we inhale, oxygen enters our lungs and diffuses across the thin alveolar membrane. The alveoli are tiny, air-filled sacs that provide a vast surface area for gas exchange. Oxygen in the alveoli has a higher partial pressure (105 mmHg) than in the adjacent pulmonary capillaries (40 mmHg), establishing a pressure gradient. As a result, oxygen molecules move from the alveoli into the...
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Updated: Jan 30, 2026

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Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external

Eduardo H P Pooch1,2, Georgios Agrotis3,4, Lishan Cai3,4

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European radiology
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PubMed
まとめ

Semi-supervised learning models for prostate cancer detection on MRI achieve performance comparable to fully supervised methods. This approach reduces reliance on expert annotations, enabling scalable AI diagnostic tools.

キーワード:
人工知能磁気共鳴画像法前立腺がん半教師あり学習

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科学分野:

  • 医用画像処理
  • 人工知能
  • 腫瘍学

背景:

  • MRIにおける前立腺がん検出のためのAIツールの開発は、広範な専門家アノテーションの必要性によって妨げられています。
  • 半教師あり学習は、このアノテーション負担を軽減するための潜在的なソリューションを提供します。

研究 の 目的:

  • MRIにおける悪性前立腺がん検出のための半教師あり学習モデルの診断パフォーマンスを評価すること。
  • 追加の専門家アノテーションでトレーニングされた完全に教師ありモデルと比較して、半教師ありモデルを比較すること。

主な方法:

  • PI-CAIチャレンジデータセットから1500件のMRIスキャンを利用しました。
  • 新しい教師あり半教師ありアプローチ（mtU-Net）を、教師ありおよび半教師ありnnU-Netモデルと比較しました。
  • PROMISおよびProstate158データセットで外部検証を実行し、AUCおよびAPを使用してパフォーマンスを評価しました。

主要な成果:

  • 完全に教師ありのnnU-Netが、内部および外部の検証データセットで最高のパフォーマンスを達成しました。
  • 提案された半教師ありmtU-Netは、完全に教師ありモデルに匹敵する外部検証パフォーマンスを示しました。
  • 半教師あり手法は、外部データセットで教師ありベースラインを大幅に上回りました。

結論:

  • 完全に教師あり学習は、前立腺MRI腫瘍検出において最高のパフォーマンスをもたらします。
  • 半教師あり学習アプローチは、外部検証において完全に教師ありモデルに近いパフォーマンスを示します。
  • 半教師あり学習は、専門家のアノテーションが限られている場合に価値のある戦略であり、スケーラブルなAI診断ツールの開発を促進します。