# Radiomics Boosts Deep Learning Model for IPMN Classification

arXiv: https://arxiv.org/abs/2309.05857 · HTML: https://mlsanity.com/paper/2309.05857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches](https://mlsanity.com/paper/1801.03230/) (2018)
- [Dynamic Linear Transformer for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/2206.00771/) (2022)
- [INN: Inflated Neural Networks for IPMN Diagnosis](https://mlsanity.com/paper/1907.00437/) (2019)
- [Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images](https://mlsanity.com/paper/2206.10531/) (2022)

## Built on by (most cited first, 3 of 3)

- [Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques](https://mlsanity.com/paper/2404.17064/) (2024)
- [Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasm](https://mlsanity.com/paper/2507.22017/) (2025)
- [Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classification](https://mlsanity.com/paper/2511.17635/) (2025)

## Abstract (arXiv)

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy).