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Self-Awareness and Its Effects01:21

Self-Awareness and Its Effects

316
Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
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Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Altered States of Awareness01:06

Altered States of Awareness

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Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
The ingestion of substances like stimulants or hallucinogens leads to chemical alterations in the brain...
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Subconsciousness and No Awareness01:15

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The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
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Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

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Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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Video Experimental Relacionado

Updated: Feb 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Aprendizaje Múltiple de Instancias Consciente del Ranking para la Clasificación de Diapositivas Histopatológicas:

Ho Heon Kim1,2, Gisu Hwang1, Won Chan Jeong1

  • 1AI R&D Center, Seegene Medical Foundation, Seoul, Republic of Korea.

JMIR medical informatics
|February 4, 2026
PubMed
Resumen
Este resumen es generado por máquina.

La inducción de rango, un nuevo marco de aprendizaje múltiple de instancias (MIL), utiliza eficazmente anotaciones parciales de expertos para mejorar la clasificación a nivel de diapositiva en patología digital. Este enfoque demuestra robustez en escenarios del mundo real con anotaciones limitadas o imprecisas.

Palabras clave:
entrenamiento eficiente en datospatología digitalaprendizaje para clasificarmixto supervisiónaprendizaje múltiple de instanciasaprendizaje débilmente supervisadoimagen completa de diapositiva

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Área de la Ciencia:

  • Patología digital; Patología computacional; Aprendizaje automático en medicina

Sus antecedentes:

  • El aprendizaje múltiple de instancias (MIL) es una técnica clave para la clasificación a nivel de diapositiva en patología digital.
  • Los métodos MIL actuales a menudo no aprovechan eficazmente las anotaciones parciales de expertos.
  • Las anotaciones de expertos, aunque sean parciales, pueden mejorar significativamente los modelos de aprendizaje supervisado.

Objetivo del estudio:

  • Desarrollar y evaluar un marco MIL consciente del ranking, denominado inducción de rango.
  • Integrar anotaciones parciales de expertos en MIL para mejorar la clasificación a nivel de diapositiva.
  • Evaluar el rendimiento del marco bajo restricciones de anotación realistas.

Principales métodos:

  • Se desarrolló la inducción de rango, un enfoque MIL que utiliza una pérdida de rango por pares inspirada en RankNet.
  • El marco prioriza los parches de relevancia diagnóstica al asignar mayor atención a las regiones anotadas.
  • Se evaluó en los conjuntos de datos Camelyon16, DigestPath2019 y SMF-estómago bajo varios escenarios de anotación.

Principales resultados:

  • La inducción de rango logró altas puntuaciones AUROC: 0.839 (Camelyon16), 0.995 (DigestPath2019) y 0.875 (SMF-estómago).
  • El modelo demostró robustez en regímenes de bajos datos, manteniendo un AUROC de 0.761 con datos de entrenamiento reducidos.
  • Se logró un rendimiento casi saturado con solo el 20% de anotaciones parciales a nivel de diapositiva.

Conclusiones:

  • La integración de anotaciones de expertos a través de la supervisión basada en el ranking mejora el rendimiento de la clasificación basada en MIL.
  • La inducción de rango demuestra ser práctica y robusta para aplicaciones de patología digital con anotaciones limitadas, imprecisas o dispersas.