mlsanityData to 7 Oct 2026

Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

arXiv 2408.12534 ↗First posted 22 Aug 2024eess.IV cs.AI cs.CVcited by 20

Not on the map. Why?

Abstract (arXiv)

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://codalab.lisn.upsaclay.fr/competitions/12239.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 30 of its 55 references that are in the map. First posted on arXiv 22 Aug 2024.

Builds on

30 of 55 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  6. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  7. 2021The Medical Segmentation Decathloncited by 620
  8. 2021MetaFormer Is Actually What You Need for Visioncited by 523
  9. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  10. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  11. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  12. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
18 more
  1. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  2. 2022Metrics reloaded: Recommendations for image analysis validationcited by 190
  3. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 188
  4. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  5. 2024Towards Generalizable Tumor Synthesiscited by 65
  6. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  7. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60
  8. 2019BIAS: Transparent reporting of biomedical image analysis challengescited by 57
  9. 2023Label-Free Liver Tumor Segmentationcited by 56
  10. 2023Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challengecited by 50
  11. 2023Continual Learning for Abdominal Multi-Organ and Tumor Segmentationcited by 43
  12. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  13. 2022Learning from partially labeled data for multi-organ and tumor segmentationcited by 18
  14. 2022PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentationcited by 16
  15. 2023CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scanscited by 15
  16. 2023Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Networkcited by 5
  17. 2023Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CTcited by 3
  18. 2024Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reportscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 10; 2026: 6.

20 papers.

Sort:

By area:

  1. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  2. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  3. 2024Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?cited by 31
  4. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 22
  5. 2025PanTS: The Pancreatic Tumor Segmentation Datasetcited by 15
  6. 2025LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imagingcited by 11
  7. 2025Learning Segmentation from Radiology Reportscited by 11
  8. 2025MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Datasetcited by 6
  9. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  10. 2024Autoregressive Sequence Modeling for 3D Medical Image Representationcited by 4
  11. 2026Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretrainingcited by 4
  12. 2024DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learningcited by 3
8 more
  1. 2025Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challengescited by 2
  2. 2025State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicabilitycited by 1
  3. 2025Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomographycited by 1
  4. 2026From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysiscited by 0
  5. 2026MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysiscited by 0
  6. 2026MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Modelscited by 0
  7. 2026SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Imagescited by 0
  8. 2026Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Datasetcited by 0