Interactive decision support system for lung cancer segmentation

TL;DR

This paper introduces a novel interactive decision support system for lung cancer segmentation using deep neural networks, emphasizing user feedback and system evaluation improvements, showing superior performance over previous methods.

Contribution

A new interactive CIDSS for lung lesion segmentation is proposed, with novel evaluation criteria and feedback simulation, advancing the state-of-the-art in clinical decision support systems.

Findings

Outperforms previous segmentation approaches in accuracy

Enhances user experience in clinical decision support

Proposes new metrics for system and feedback quality

Abstract

This paper studies Clinical Intelligent Decision Support Systems (CIDSSs) for lung cancer segmentation, which are based on deep neural nets. A new interactive CIDSS is proposed and compared with previous approaches. Addition-ally, the purpose uncertainty problem in building interactive systems is discussed, and criteria for measuring both quality and amount of user feedback are proposed. In order to automate system evaluation, a new algorithm was used to simulate expert feedback. The proposed interactive CIDSS outperforms previous approaches (both interactive and noninteractive) on the task of lung lesion segmentation. This ap-proach looks promising both in terms of quality and expert user experience. At the same time, this paper discusses a bunch of possible modifications that can be done to improve both evaluation criteria and proposed CIDSS in future works.