# Joint Shape Representation and Classification for Detecting PDAC

arXiv: https://arxiv.org/abs/1804.10684 · HTML: https://mlsanity.com/paper/1804.10684/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

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

- [Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer](https://mlsanity.com/paper/2308.11389/) (2023)
- [Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection](https://mlsanity.com/paper/2210.12095/) (2022)
- [Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT](https://mlsanity.com/paper/2303.00942/) (2023)

## Abstract (arXiv)

We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task with little training data. We propose a two-stage framework, which first segments the pancreas into a binary mask, then compresses the mask into a shape vector and performs abnormality classification. Shape representation and classification are performed in a joint manner, both to exploit the knowledge that PDAC often changes the shape of the pancreas and to prevent over-fitting. Experiments are performed on 300 normal scans and 136 PDAC cases. We achieve a specificity of 90.2% (false alarm occurs on less than 1/10 normal cases) at a sensitivity of 80.2% (less than 1/5 PDAC cases are not detected), which show promise for clinical applications.