Removing Zero Variance Units of Deep Models for COVID-19 Detection
Por:
Garcia-Ramirez, Jesus, Escalante-Ramirez, Boris, Montiel, Jimena Olveres
Publicada:
1 ene 2023
Resumen:
Deep Learning has been used for several applications including the
analysis of medical images. Some transfer learning works show that an
improvement in performance is obtained if a pre-trained model on
ImageNet is transferred to a new task. Taking into account this, we
propose a method that uses a pre-trained model on ImageNet to fine-tune
it for Covid-19 detection. After the fine-tuning process, the units that
produce a variance equal to zero are removed from the model. Finally, we
test the features of the penultimate layer in different classifiers
removing those that are less important according to the f-test. The
results produce models with fewer units than the transferred model.
Also, we study the attention of the neural network for classification.
Noise and metadata printed in medical images can bias the performance of
the neural network and it obtains poor performance when the model is
tested on new data. We study the bias of medical images when raw and
masked images are used for training deep models using a transfer
learning strategy. Additionally, we test the performance on novel data
in both models: raw and masked data.
Filiaciones:
Garcia-Ramirez, Jesus:
Univ Nacl Autonoma Mexico, Fac Ingn, Mexico City 04510, Mexico
Escalante-Ramirez, Boris:
Univ Nacl Autonoma Mexico, Fac Ingn, Mexico City 04510, Mexico
Univ Nacl Autonoma Mexico, Ctr Estudios Comp Avanzada, Mexico City 04510, Mexico
Montiel, Jimena Olveres:
Univ Nacl Autonoma Mexico, Fac Ingn, Mexico City 04510, Mexico
Univ Nacl Autonoma Mexico, Ctr Estudios Comp Avanzada, Mexico City 04510, Mexico
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