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MG-HGLNet: Un marco de aprendizaje jerárquico geométrico-semántico de grano mixto con prototipos dinámicos para la
Xiangxin Wang1, Yangfan Chen2, Yi Wu2
1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China.
Una nueva red de aprendizaje profundo, MG-HGLNet, mejora el diagnóstico automatizado de la enfermedad coronaria (EC) a partir de escaneos de angiografía coronaria por tomografía computarizada (CCTA). Califica con precisión la estenosis y clasifica la placa, incluso con datos limitados.
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