Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data
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2021-03-30
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MDPI (Switzerland)
Resumen
Deep learning requires a large amount of data to perform well. However, the field of
medical image analysis suffers from a lack of sufficient data for training deep learning models.
Moreover, medical images require manual labeling, usually provided by human annotators coming
from various backgrounds. More importantly, the annotation process is time-consuming, expensive,
and prone to errors. Transfer learning was introduced to reduce the need for the annotation process
by transferring the deep learning models with knowledge from a previous task and then by fine-
tuning them on a relatively small dataset of the current task. Most of the methods of medical image
classification employ transfer learning from pretrained models, e.g., ImageNet, which has been
proven to be ineffective. This is due to the mismatch in learned features between the natural image,
e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated
models. In this paper, we propose a novel transfer learning approach to overcome the previous
drawbacks by means of training the deep learning model on large unlabeled medical image datasets
and by next transferring the knowledge to train the deep learning model on the small amount
of labeled medical images. Additionally, we propose a new deep convolutional neural network
(DCNN) model that combines recent advancements in the field. We conducted several experiments
on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks.
According to the reported results, it has been empirically proven that the proposed approach can
significantly improve the performance of both classification scenarios. In terms of skin cancer, the
proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with
the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively,
when trained from scratch and using the proposed approach in the case of the breast cancer scenario.
Finally, we concluded that our method can possibly be applied to many medical imaging problems
in which a substantial amount of unlabeled image data is available and the labeled image data is
limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the
same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify
them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-
score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using
double-transfer learning.
Descripción
El trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, siendo este uno de los varios artículos científicos que fueron desarrollados y publicados durante y después de la tesis doctoral del Dr. Laith Alzubaidi.
Palabras clave
medical image analysis, convolution neural network, transfer learning, deep learning
Citación
Alzubaidi, L.; Al-Amidie, M.; Al-Asadi, A.; Humaidi, A.J.; Al-Shamma, O.; Fadhel, M.A.; Zhang, J.; Santamaría, J.; Duan, Y. Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data. Cancers 2021, 13, 1590. https://doi.org/10.3390/cancers13071590