An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability

TL;DR

This paper proposes an AI-augmented, interpretable framework to assist clinicians in detecting liver metastases from colorectal cancer, aiming to improve accuracy, trust, and efficiency in metastasis assessment.

Contribution

It introduces an interactive, interpretable AI system specifically designed for liver metastasis detection, addressing trust and usability issues in clinical AI applications.

Findings

Enhanced interpretability of AI models for metastasis detection

Improved clinician-AI collaboration in assessment process

Potential reduction in time and subjectivity of metastasis evaluation

Abstract

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths worldwide. Most CRC deaths are the result of progression of metastases. The assessment of metastases is done using the RECIST criterion, which is time consuming and subjective, as clinicians need to manually measure anatomical tumor sizes. AI has many successes in image object detection, but often suffers because the models used are not interpretable, leading to issues in trust and implementation in the clinical setting. We propose a framework for an AI-augmented system in which an interactive AI system assists clinicians in the metastasis assessment. We include model interpretability to give explanations of the reasoning of the underlying models.