Video Experimental Relacionado
Updated: Feb 15, 2026
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
10.9K
Segmentación Automatizada de Lesiones de Pterigión Utilizando Redes de Aprendizaje Profundo Multiescala
1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia.
Experimental eye research
|February 13, 2026
Resumen
Este estudio presenta un método de aprendizaje profundo para la detección temprana de pterigión. El mejor modelo mapea con precisión las lesiones oculares, mejorando la predicción de la gravedad y previniendo la pérdida de visión.
Área de la Ciencia:
- Oftalmología
- Imágenes Médicas
- Ciencias de la Computación
Sus antecedentes:
- El pterigión es una afección ocular que requiere detección temprana para prevenir el deterioro visual.
- La medición precisa de la invasión del tejido fibrovascular es crucial para evaluar la gravedad del pterigión.
- El aprendizaje profundo ofrece un enfoque prometedor para la cuantificación automatizada de lesiones de pterigión.
Objetivo del estudio:
- Desarrollar un método de segmentación semántica para el mapeo preciso de lesiones de pterigión.
- Explorar redes de aprendizaje profundo multiescala para capturar características variables de las lesiones.
- Mejorar la predicción de la gravedad del pterigión mediante la extracción precisa de lesiones.
Principales métodos:
- Se implementaron módulos de aprendizaje profundo multiescala (SPP, ASPP) dentro de una arquitectura UNet.
- Se investigaron patrones de flujo igual (EF) y flujo de cascada (WF) para la construcción de rutas paralelas.
- Se evaluó el rendimiento de la segmentación utilizando la distancia de Hausdorff.
Principales resultados:
- La arquitectura UNet con tres rutas paralelas del módulo EF-ASPP logró la menor distancia de Hausdorff (16,75 píxeles).
- Este enfoque multiescala capturó eficazmente escalas variables de lesiones de pterigión.
- El mapeo preciso de lesiones facilitó una mejor predicción de la gravedad del pterigión.
Conclusiones:
- Las redes de aprendizaje profundo multiescala, en particular EF-ASPP dentro de UNet, muestran un potencial significativo para la segmentación de pterigión.
- La segmentación precisa de las lesiones de pterigión ayuda en la detección y el manejo tempranos, mitigando los riesgos de deterioro visual.
- El trabajo futuro podría explorar diversas arquitecturas de red para un rendimiento mejorado, equilibrando la precisión y el costo computacional.
Videos de Conceptos Relacionados
02:26Protein Networks
4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
02:26Protein Networks
2.9K
2.9K
02:18Network Covalent Solids
16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
01:14Avoidance Learning and Learned Helplessness
2.7K
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...
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...
2.7K
01:27Associative Learning
1.5K
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...
Classical conditioning, also known...
1.5K
01:22Purposive Learning
522
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...
522