# PanTS: The Pancreatic Tumor Segmentation Dataset

arXiv: https://arxiv.org/abs/2507.01291 · HTML: https://mlsanity.com/paper/2507.01291/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 73 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 30 of 31)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking](https://mlsanity.com/paper/2407.16697/) (2024)
- [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1912.09628/) (2019)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [Continual Learning for Abdominal Multi-Organ and Tumor Segmentation](https://mlsanity.com/paper/2306.00988/) (2023)
- [MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images](https://mlsanity.com/paper/2203.12362/) (2022)
- [RadGPT: Constructing 3D Image-Text Tumor Datasets](https://mlsanity.com/paper/2501.04678/) (2025)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?](https://mlsanity.com/paper/2411.03670/) (2024)
- [Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge](https://mlsanity.com/paper/2408.12534/) (2024)
- [Scalable Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1906.05956/) (2019)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis](https://mlsanity.com/paper/2308.03008/) (2023)
- [Text-Driven Tumor Synthesis](https://mlsanity.com/paper/2412.18589/) (2024)
- [The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset](https://mlsanity.com/paper/2405.19595/) (2024)
- [Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?](https://mlsanity.com/paper/2402.19423/) (2024)
- [Embracing Massive Medical Data](https://mlsanity.com/paper/2407.04687/) (2024)

## Built on by (most cited first, 16 of 16)

- [Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data](https://mlsanity.com/paper/2510.14831/) (2025)
- [Learning Segmentation from Radiology Reports](https://mlsanity.com/paper/2507.05582/) (2025)
- [Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks](https://mlsanity.com/paper/2510.14803/) (2025)
- [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025)
- [RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology](https://mlsanity.com/paper/2605.10761/) (2026)
- [See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement](https://mlsanity.com/paper/2512.07251/) (2025)
- [Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges](https://mlsanity.com/paper/2512.19091/) (2025)
- [PanGuide3D: Cohort-Robust Pancreas Tumor Segmentation via Probabilistic Pancreas Conditioning and a Transformer Bottleneck](https://mlsanity.com/paper/2604.20981/) (2026)
- [Beyond Masks: The Case for Medical Image Parsing](https://mlsanity.com/paper/2605.11438/) (2026)
- [Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions](https://mlsanity.com/paper/2607.19378/) (2026)
- [Report Supervision](https://mlsanity.com/paper/2608.27668/) (2026)
- [Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering](https://mlsanity.com/paper/2609.07178/) (2026)
- [Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts](https://mlsanity.com/paper/2609.08879/) (2026)
- [RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports](https://mlsanity.com/paper/2609.35637/) (2026)
- [Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset](https://mlsanity.com/paper/2609.36436/) (2026)
- [FLASHSWIN: Unlocking Large Windows and Dense Tokens in Swin Vision Transformers with Memory Efficient Attention](https://mlsanity.com/paper/2610.04664/) (2026)

## Abstract (arXiv)

PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation compared to those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16x larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.