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Brain tumor grade classification using multi-step pre-training
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DOI：10.1002/ima.23008.png)
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Abstract
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Medical images offer a non-invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data-intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between the datasets used for pre-training (e.g., ImageNet) and the target datasets, like medical images, negatively impacts the transfer learning results. Recently, many researchers have used additional pre-training called domain-adaptive pre-training (DAPT) using the data from the target domain (e.g., medical images) before using the model on the target tasks to achieve superior performance. This study proposes a variant of DAPT by performing it on a subset of the architecture. It has achieved state-of-the-art performance for brain tumor grading on the BraTS 2019 while being computationally efficient.
Keywords:
brain tumor
computational efficiency
domain adaptive pre-training
transfer learning
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