Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
This work addresses the challenge of unreliable IPMN classification in medical imaging for pancreatic cancer diagnosis, but it is incremental as it applies an existing transformer method to a new medical domain.
The authors tackled the problem of classifying Intraductal Papillary Mucosal Neoplasms (IPMN) in MRI images to improve early detection and grading, and they showed that their transformer-based model exploits pre-training better than standard convolutional neural networks, enabling better interpretation of results.
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results.