An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability
Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Hunt, Xin J., Abbey, Ralph, Tharrington, Ricky, Huiskens, Joost, Wesdorp, Nina
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2962350846