  Biomedical image analysis is of paramount importance for the advancement of
healthcare and medical research. Although conventional convolutional neural
networks (CNNs) are frequently employed in this domain, facing limitations in
capturing intricate spatial and temporal relationships at the pixel level due
to their reliance on fixed-sized windows and immutable filter weights
post-training. These constraints impede their ability to adapt to input
fluctuations and comprehend extensive long-range contextual information. To
overcome these challenges, a novel architecture based on self-attention
mechanisms as an alternative to conventional CNNs.The proposed model utilizes
attention-based mechanisms to surpass the limitations of CNNs. The key
component of our strategy is the combination of non-overlapping (vanilla
patching) and novel overlapped Shifted Patching Techniques (S.P.T.s), which
enhances the model's capacity to capture local context and improves
generalization. Additionally, we introduce the Lancoz5 interpolation technique,
which adapts variable image sizes to higher resolutions, facilitating better
analysis of high-resolution biomedical images. Our methods address critical
challenges faced by attention-based vision models, including inductive bias,
weight sharing, receptive field limitations, and efficient data handling.
Experimental evidence shows the effectiveness of proposed model in generalizing
to various biomedical imaging tasks. The attention-based model, combined with
advanced data augmentation methodologies, exhibits robust modeling capabilities
and superior performance compared to existing approaches. The integration of
S.P.T.s significantly enhances the model's ability to capture local context,
while the Lancoz5 interpolation technique ensures efficient handling of
high-resolution images.
