Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework

Abstract

Cancer decision-making is a complex process that can be exacerbated by the limited availability of oncological expertise. This is particularly true in rural areas and settings with fewer resources. Recently, there has been an interest in the potential of artificial intelligence in reliable computer-aided diagnosis tools in such settings. Nevertheless, the majority of deep learning algorithms are resource hungry in terms of data and storage requirements. In this work, we propose a novel lightweight deep learning model for histological tumor classification through a Joint Sparsity-Quantization Aware Training framework. Extensive experiments were conducted to evaluate the proposed framework. Promising performance has been achieved compared to the most relevant state-of-the-art work with a classification accuracy of 94.26% and an average $5\times $ reduction in the memory footprint. This work aims at opening doors toward efficient point-of-care diagnostic devices suitable for environments with limited resources.

Keywords

• Deep learning
• histopathology
• quantization
• pruning
• transfer learning
• medical image analysis