# Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

arXiv: https://arxiv.org/abs/1807.02941 · HTML: https://mlsanity.com/paper/1807.02941/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 24 of 24)

- [CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios](https://mlsanity.com/paper/2404.15272/) (2024)
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- [CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans](https://mlsanity.com/paper/2301.12291/) (2023)
- [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024)
- [Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data](https://mlsanity.com/paper/2310.15098/) (2023)
- [Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography](https://mlsanity.com/paper/2111.15409/) (2021)
- [Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT](https://mlsanity.com/paper/2210.14297/) (2022)
- [LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning](https://mlsanity.com/paper/2407.13217/) (2024)
- [Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data](https://mlsanity.com/paper/2112.12665/) (2021)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans](https://mlsanity.com/paper/2307.04525/) (2023)
- [Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinoma](https://mlsanity.com/paper/2310.00639/) (2023)
- [Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports](https://mlsanity.com/paper/2405.14230/) (2024)
- [A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT Scans](https://mlsanity.com/paper/2505.01644/) (2025)
- [UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model](https://mlsanity.com/paper/2507.18362/) (2025)
- [Improved Pancreatic Tumor Detection by Utilizing Clinically-Relevant Secondary Features](https://mlsanity.com/paper/2208.03581/) (2022)
- [Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation](https://mlsanity.com/paper/2210.08652/) (2022)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [Integrating features from lymph node stations for metastatic lymph node detection](https://mlsanity.com/paper/2301.03202/) (2023)
- [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)
- [Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology](https://mlsanity.com/paper/2308.05782/) (2023)
- [CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2505.18958/) (2025)

## Abstract (arXiv)

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of 94.1% at a specificity of 98.5%, which demonstrates the potential to make a clinical impact.