# Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

arXiv: https://arxiv.org/abs/2404.17064 · HTML: https://mlsanity.com/paper/2404.17064/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing](https://mlsanity.com/paper/2303.07428/) (2023)
- [Dynamic Linear Transformer for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/2206.00771/) (2022)
- [Radiomics Boosts Deep Learning Model for IPMN Classification](https://mlsanity.com/paper/2309.05857/) (2023)
- [Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans](https://mlsanity.com/paper/2312.11480/) (2023)

## Built on by (most cited first, 2 of 2)

- [Pediatric Pancreas Segmentation from MRI Scans with Deep Learning](https://mlsanity.com/paper/2506.15908/) (2025)
- [Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer](https://mlsanity.com/paper/2609.13043/) (2026)

## Abstract (arXiv)

Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizing the critical need for accurate detection and assessment in pancreatitis diagnosis and management. This study textitintroduces a novel CT dataset sourced from 255 patients with pancreatic diseases, featuring annotated pancreas segmentation masks and corresponding diagnostic labels for peri-pancreatic edema condition. With the novel dataset, we first evaluate the efficacy of the textitLinTransUNet model, a linear Transformer based segmentation algorithm, to segment the pancreas accurately from CT imaging data. Then, we use segmented pancreas regions with two distinctive machine learning classifiers to identify existence of peri-pancreatic edema: deep learning-based models and a radiomics-based eXtreme Gradient Boosting (XGBoost). The LinTransUNet achieved promising results, with a dice coefficient of 80.85%, and mIoU of 68.73%. Among the nine benchmarked classification models for peri-pancreatic edema detection, textitSwin-Tiny transformer model demonstrated the highest recall of 98.85 ± 0.42 and precision of 98.38± 0.17. Comparatively, the radiomics-based XGBoost model achieved an accuracy of 79.61±4.04 and recall of 91.05±3.28, showcasing its potential as a supplementary diagnostic tool given its rapid processing speed and reduced training time. Our code is available urlhttps://github.com/NUBagciLab/Peri-Pancreatic-Edema-Detection.