An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability
This addresses the need for more efficient and trustworthy metastasis assessment in clinical settings, but appears incremental as it builds on existing AI object detection methods with added interpretability.
The paper tackles the problem of time-consuming and subjective manual assessment of liver metastases in colorectal cancer by proposing an AI-augmented framework that assists clinicians with interpretable lesion detection, though no concrete performance numbers are provided.
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.