Harnessing The Power of Attention For Patch-Based Biomedical Image
  Classification
Gousia Habib Shaima Qureshi Malik ishfaq
Biomedical image analysis can be facilitated by an innovative architecture
rooted in self-attention mechanisms. The traditional convolutional neural
network (CNN), characterized by fixed-sized windows, needs help capturing
intricate spatial and temporal relations at the pixel level. The immutability
of CNN filter weights post-training further restricts input fluctuations.
Recognizing these limitations, we propose a new paradigm of attention-based
models instead of convolutions. As an alternative to traditional CNNs, these
models demonstrate robust modelling capabilities and the ability to grasp
comprehensive long-range contextual information efficiently. Providing a
solution to critical challenges faced by attention-based vision models such as
inductive bias, weight sharing, receptive field limitations, and data handling
in high resolution, our work combines non-overlapping (vanilla patching) with
novel overlapped Shifted Patching Techniques (S.P.T.s) to induce local context
that enhances model generalization. Moreover, we examine the novel Lancoz5
interpolation technique, which adapts variable image sizes to higher
resolutions. Experimental evidence validates our model's generalization
effectiveness, comparing favourably with existing approaches. Attention-based
methods are particularly effective with ample data, especially when advanced
data augmentation methodologies are integrated to strengthen their robustness.