Improving Few Shot Adaptive Learning for Medical Image Classification using Vision Transformer(ViT)

Abstract

Medical image classification plays a pivotal role in automating disease diagnosis and treatment planning. However, the limited availability of annotated medical data poses a significant challenge for training accurate classifiers. This research paper introduces an enhanced approach to improve Few-Shot Adaptive Learning for Medical Image Classification, employing the transformative capabilities of Vision Transformer (ViT) architectures. Our proposed method uses ViTs to capture intricate spatial relation ships and contextual information inherent in medical images. To address the chal lenge of limited labeled data, we focus on improving Few-Shot Learning by intro ducing adaptive learning strategies. The integration of ViT not only enhances the model’s ability to learn complex patterns but also facilitates efficient adaptation to new classes with minimal labeled data. The model dynamically adjusts its representa tion space, allowing for efficient adaptation to diverse medical imaging scenarios with minimal labeled examples. Extensive experiments are conducted on diverse medical image datasets to validate the effectiveness of our approach. The results showcase no table improvements in classification performance compared to existing state-of-the art methods. The proposed ViT-based framework holds promise for improving the generalization and adaptability of medical image classifiers, thereby contributing to the advancement of automated medical diagnosis and treatment planning