Explainable few-shot learning with visual explanations on a low resource pneumonia dataset

The data hungry nature of neural networks acts as a barrier to their application in data-scarce scenarios like medical domain. Few-shot learning has attracted much interest recently in handling small-scale datasets. Along with the need to handle the issue of limited annotated data in each class, we also need to address the black-box nature of neural nets which hampers their application in risk-sensitive areas. Pneumonia identification in low resource scenarios along with explanations is a challenging task. Therefore, in this paper, we reveal a few-shot learning-based pneumonia classification technique along with deriving visual explanations to gain insights into the model's decision-making process. Interpretation for models’ decisions is generated by computing the importance scores based on a comparison between the values of activated neurons and the reference values. We achieve a classification accuracy of 87% and 82% for covid-chestxray-dataset and RSNA Pneumonia dataset respectively, thereby utilising 20 images per category for our experiments. Evaluation of importance scores and the change in log odds scores obtained by image morphing task demonstrate that the proposed framework outperformed the other popular gradient-based methods.