Optimizing Diagnosis in Sparse Data Environments: A Model Agnostic Meta Learning Approach
Kanishka Ranaweera, Pubudu Nishantha Pathirana · 2024
In contemporary medical diagnostic applications, the scarcity of data poses a significant challenge to achieving high accuracy and robust performance. This paper delves into the realm of sparse data environments, particularly focusing on medical imaging diagnostics using the NIH Chest X-ray dataset. Recognizing the limitations imposed by insufficient data, we propose a novel approach based on Model Agnostic Meta Learning (MAML) to navigate and mitigate these challenges effectively. Through a 20-shot MAML training strategy, the model is exposed to a limited dataset but rapidly adapts to new information, enabling it to achieve competitive diagnostic accuracies. The experimental validation, conducted on the NIH Chest X-ray dataset, demonstrates the resilience and adaptability of our proposed approach. The model exhibits robust diagnostic performance, demonstrating its potential to become a valuable tool in scenarios where acquiring large volumes of labeled data is challenging or impractical. The success of our MAML approach underscores its promise as a valuable asset for optimizing diagnostic capabilities in resource-constrained medical environments. As medical datasets continue to grow in size and complexity, the insights gained from this study open avenues for further advancements in leveraging meta-learning for improved healthcare applications within data-limited contexts.