Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images

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.

READ FULL TEXT

page 1

page 2

page 4

research
09/06/2023

Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review

Vision transformer-based methods are advancing the field of medical arti...
research
04/09/2023

Transformer Utilization in Medical Image Segmentation Networks

Owing to success in the data-rich domain of natural images, Transformers...
research
05/20/2022

Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)

Vision transformers, with their ability to more efficiently model long-r...
research
07/04/2022

Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer

With the development of computer technology, various models have emerged...
research
08/10/2022

Alternating Cross-attention Vision-Language Model for Efficient Learning with Medical Image and Report without Curation

Recent advances in vision-language pre-training have demonstrated astoun...
research
05/05/2022

Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures

The image captioning task is increasingly prevalent in artificial intell...
research
03/05/2017

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important ...

Please sign up or login with your details

Forgot password? Click here to reset