Abstract:
Limited availability of annotated medical imaging data poses
a challenge for deep learning algorithms. Although transfer learning minimizes this hurdle in general, knowledge transfer across disparate domains
is shown to be less effective. On the other hand, smaller architectures
were found to be more compelling in learning better features. Consequently, we propose a lightweight architecture that uses mixed asymmetric kernels (MAKNet) to reduce the number of parameters significantly.
Additionally, we train the proposed architecture using semi-supervised
learning to provide pseudo-labels for a large medical dataset to assist
with transfer learning. The proposed MAKNet provides better classification performance with 60 − 70% less parameters than popular architectures. Experimental results also highlight the importance of domainspecific knowledge for effective transfer learning.
