Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification

TL;DR

This paper introduces a novel lightweight knowledge distillation framework using multi-head attention for endoscopic image classification, improving model efficiency and accuracy in resource-limited medical settings.

Contribution

It proposes a relation-based knowledge distillation method with multi-head attention for better feature transfer, creating a lightweight model suitable for low-resource environments.

Findings

Achieved high accuracy with only 51.8k parameters

Outperformed existing models on KVASIR-V2 and Hyper-KVASIR datasets

Demonstrated effectiveness of multi-head attention in knowledge transfer

Abstract

Endoscopy plays a major role in identifying any underlying abnormalities within the gastrointestinal (GI) tract. There are multiple GI tract diseases that are life-threatening, such as precancerous lesions and other intestinal cancers. In the usual process, a diagnosis is made by a medical expert which can be prone to human errors and the accuracy of the test is also entirely dependent on the expert's level of experience. Deep learning, specifically Convolution Neural Networks (CNNs) which are designed to perform automatic feature learning without any prior feature engineering, has recently reported great benefits for GI endoscopy image analysis. Previous research has developed models that focus only on improving performance, as such, the majority of introduced models contain complex deep network architectures with a large number of parameters that require longer training times. However, there is a lack of focus on developing lightweight models which can run in low-resource environments, which are typically encountered in medical clinics. We investigate three KD-based learning frameworks, response-based, feature-based, and relation-based mechanisms, and introduce a novel multi-head attention-based feature fusion mechanism to support relation-based learning. Compared to the existing relation-based methods that follow simplistic aggregation techniques of multi-teacher response/feature-based knowledge, we adopt the multi-head attention technique to provide flexibility towards localising and transferring important details from each teacher to better guide the student. We perform extensive evaluations on two widely used public datasets, KVASIR-V2 and Hyper-KVASIR, and our experimental results signify the merits of our proposed relation-based framework in achieving an improved lightweight model (only 51.8k trainable parameters) that can run in a resource-limited environment.