Six types of heterogeneous pseudo-color images after U-Net network segmentation.

Six types of heterogeneous pseudo-color images after U-Net network segmentation.

<p><b>(a)</b> Pseudo-color image of heterogeneity1; <b>(b)</b> Pseudo-color image of heterogeneity2; <b>(c)</b> Pseudo-color image of heterogeneity3; <b>(d)</b> Pseudo-color image of heterogeneity4; <b>(e)</b> Pseudo-color image of he...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Fulong Liu (22614787) (author)
مؤلفون آخرون: Siyuan Huang (5470118) (author), Jie Gao (10266) (author), Xin Zhou (54275) (author), Junqi Wang (728963) (author)
منشور في: 2025
الموضوعات:
Medicine
Biotechnology
Cancer
Science Policy
Space Science
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
net semantic segmentation
global average pooling
experimental results show
deep learning networks
color images obtained
spectral fusion pseudo
improving detection accuracy
m_d significantly improved
5 hz m_d
image quality individually
accurate heterogeneous classification
spectral transmission images
fa images processed
highest classification accuracy
demodulation frame accumulation
original vgg16 model
spectral images
heterogeneous classification
frame accumulation
classification accuracy
transmission process
model achieved
highest signal
m_d alone
scattering effects
proved superior
paper achieves
obvious absorption
noise ratio
gradual improvement
different combinations
clinical application
biological tissues
batch normalization
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