mlsanityData to 7 Oct 2026

SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations

arXiv 2301.07074 ↗First posted 17 Jan 2023cs.CV cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an expensive effort, especially for 3D images. One potential solution is to aggregate knowledge from partially annotated datasets from multiple groups to collaboratively train global models using Federated Learning. To this end, we propose SegViz, a federated learning-based framework to train a segmentation model from distributed non-i.i.d datasets with partial annotations. The performance of SegViz was compared against training individual models separately on each dataset as well as centrally aggregating all the datasets in one place and training a single model. The SegViz framework using FedBN as the aggregation strategy demonstrated excellent performance on the external BTCV set with dice scores of 0.93, 0.83, 0.55, and 0.75 for segmentation of liver, spleen, pancreas, and kidneys, respectively, significantly (p<0.05) better (except spleen) than the dice scores of 0.87, 0.83, 0.42, and 0.48 for the baseline models. In contrast, the central aggregation model significantly (p<0.05) performed poorly on the test dataset with dice scores of 0.65, 0, 0.55, and 0.68. Our results demonstrate the potential of the SegViz framework to train multi-task models from distributed datasets with partial labels. All our implementations are open-source and available at https://anonymous.4open.science/r/SegViz-B746

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

16 of 23 references.

References · 16

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  3. 2021The Medical Segmentation Decathloncited by 620
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  5. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  6. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 282
  7. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  8. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
  9. 2019The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomescited by 168
  10. 2020DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetscited by 94
  11. 2020Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstractioncited by 70
  12. 2020Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasetscited by 26
4 more
  1. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  2. 2018Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Imagescited by 14
  3. 2021Cross-Domain Federated Learning in Medical Imagingcited by 6
  4. 2022Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priorscited by 4

Built on by

Not cited yet.