Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.7K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.7K
Types Of Transformers01:16

Types Of Transformers

1.5K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.5K
Bacterial Transformation01:33

Bacterial Transformation

59.9K
In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
59.9K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.2K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.2K
Force Classification01:22

Force Classification

2.4K
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,...
2.4K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Improving preoperative risk stratification in colorectal liver metastases: a multi-institutional evaluation of multimodal prediction models.

Scientific reports·2026
Same author

3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.

Magnetic resonance in medicine·2026
Same author

Molecular and Clinical Determinants of Targeted Therapy Treatment in Biliary Tract Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Pembrolizumab and olaparib in homologous-recombination-deficient metastatic pancreatic cancer: the phase 2 POLAR trial.

Nature medicine·2026
Same author

The LELEX initiative to enhance cancer imaging communication: development of a consensus-based structured reporting template and lexicon for general oncologic imaging.

Radiologia brasileira·2026
Same author

How I Do It: Systemic Therapies for Primary Liver Cancers.

Radiology·2026

Video Experimental Relacionado

Updated: Feb 1, 2026

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
13:57

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

1.3K

Pipeline de Transformador Auto-Supervisado para Segmentación y Clasificación de Tumores Hepáticos

Ramtin Mojtahedi1, Mohammad Hamghalam1,2, Jacob J Peoples1

  • 1School of Computing, Queen's University, Kingston, ON, Canada.

JCO clinical cancer informatics
|January 30, 2026
PubMed
Resumen

Este estudio presenta un pipeline de aprendizaje auto-supervisado que mejora la segmentación y clasificación de tumores hepáticos. El método mejora la precisión en la detección de tumores hepáticos y la clasificación de sus tipos, ayudando en la planificación del tratamiento.

Palabras clave:
aprendizaje auto-supervisadosegmentación de tumores hepáticosclasificación de tumores hepáticosaprendizaje profundoimágenes médicasresonancia magnéticatomografía computarizadaplanificación del tratamientocáncer de hígadointeligencia artificial

Más Videos Relacionados

Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain
06:13

Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain

Published on: March 1, 2024

1.8K
The Influence of Liver Resection on Intrahepatic Tumor Growth
07:55

The Influence of Liver Resection on Intrahepatic Tumor Growth

Published on: April 9, 2016

9.9K

Videos de Experimentos Relacionados

Last Updated: Feb 1, 2026

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
13:57

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

1.3K
Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain
06:13

Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain

Published on: March 1, 2024

1.8K
The Influence of Liver Resection on Intrahepatic Tumor Growth
07:55

The Influence of Liver Resection on Intrahepatic Tumor Growth

Published on: April 9, 2016

9.9K

Área de la Ciencia:

  • Imágenes Médicas; Inteligencia Artificial; Oncología

Sus antecedentes:

  • La detección y segmentación precisas de tumores hepáticos son cruciales para un tratamiento eficaz y el seguimiento de la progresión de la enfermedad. Los métodos actuales a menudo requieren conjuntos de datos anotados extensos, lo que limita su aplicación generalizada.

Objetivo del estudio:

  • Desarrollar un pipeline de extremo a extremo que utilice preentrenamiento auto-supervisado para mejorar la segmentación y clasificación de tumores hepáticos. Reducir la dependencia de grandes conjuntos de datos anotados para entrenar modelos de IA en imágenes médicas.

Principales métodos:

  • Se preentrenó un codificador de red basado en transformadores utilizando aprendizaje auto-supervisado en imágenes de TC abdominales sin etiquetar. La red de segmentación se ajustó para la segmentación del hígado y los tumores, seguida de la clasificación de los tipos de tumores (ICC, HCC, CRLM) utilizando una CNN preentrenada (Inception-v3). El modelo se evaluó en 459 imágenes internas y un conjunto de datos público independiente de 40 imágenes.

Principales resultados:

  • El preentrenamiento auto-supervisado mejoró significativamente las métricas de segmentación en comparación con una línea de base supervisada, aumentando el Coeficiente de Similitud de Dice (DSC) para el hígado en un 6,4% y para los tumores en un 6,0%. La distancia de Hausdorff (HD95) se redujo en 32,97 mm para el hígado y 3,2 mm para los tumores. La clasificación del tipo de tumor logró una alta precisión (96%) y un Área Bajo la Curva (AUC) de 0,98, con un rendimiento comparable en datos de validación externa.

Conclusiones:

  • El pipeline auto-supervisado y de extremo a extremo propuesto mejora eficazmente la precisión de la segmentación y clasificación de tumores hepáticos. Este enfoque apoya una evaluación radiológica, planificación del tratamiento y pronóstico más fiables para pacientes con cáncer de hígado. El método demuestra el potencial del aprendizaje auto-supervisado para superar las limitaciones de datos en la IA médica.