Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos

TL;DR

Adaptify is a novel adaptation scheme that improves the stability and consistency of frame classification in atrophic gastritis videos by integrating an auxiliary model's knowledge into the primary model.

Contribution

The paper introduces Adaptify, a new method where a primary model learns from an auxiliary model's decisions to enhance classification reliability in real-world scenarios.

Findings

Significant improvement in output stability and consistency.

Effective knowledge integration from auxiliary to primary model.

Enhanced reliability of gastritis video classification.

Abstract

Atrophic gastritis is a significant risk factor for developing gastric cancer. The incorporation of machine learning algorithms can efficiently elevate the possibility of accurately detecting atrophic gastritis. Nevertheless, when the trained model is applied in real-life circumstances, its output is often not consistently reliable. In this paper, we propose Adaptify, an adaptation scheme in which the model assimilates knowledge from its own classification decisions. Our proposed approach includes keeping the primary model constant, while simultaneously running and updating the auxiliary model. By integrating the knowledge gleaned by the auxiliary model into the primary model and merging their outputs, we have observed a notable improvement in output stability and consistency compared to relying solely on either the main model or the auxiliary model.