mlsanityData to 7 Oct 2026

Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT

arXiv 2211.05942 ↗First posted 11 Nov 2022eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

For more clinical applications of deep learning models for medical image segmentation, high demands on labeled data and computational resources must be addressed. This study proposes a coarse-to-fine framework with two teacher models and a student model that combines knowledge distillation and cross teaching, a consistency regularization based on pseudo-labels, for efficient semi-supervised learning. The proposed method is demonstrated on the abdominal multi-organ segmentation task in CT images under the MICCAI FLARE 2022 challenge, with mean Dice scores of 0.8429 and 0.8520 in the validation and test sets, respectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 11 Nov 2022.

Builds on

26 of 35 references.

References · 26

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  5. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  6. 2020Knowledge Distillation: A Surveycited by 1,578
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  8. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  9. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  10. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  11. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  12. 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
14 more
  1. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  2. 2019Structured Knowledge Distillation for Dense Predictioncited by 277
  3. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
  4. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
  5. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 188
  6. 2018ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentationcited by 133
  7. 2021Semi-supervised Left Atrium Segmentation with Mutual Consistency Trainingcited by 104
  8. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  9. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 99
  10. 2021Efficient Medical Image Segmentation Based on Knowledge Distillationcited by 43
  11. 2019USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasetscited by 27
  12. 2019An attempt at beating the 3D U-Netcited by 21
  13. 2021Efficient Context-Aware Network for Abdominal Multi-organ Segmentationcited by 7
  14. 2021Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirementscited by 3

Built on by

Not cited yet.