# Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation

arXiv: https://arxiv.org/abs/2407.12611 · HTML: https://mlsanity.com/paper/2407.12611/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021)
- [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)
- [xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation](https://mlsanity.com/paper/1911.12676/) (2019)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction](https://mlsanity.com/paper/2001.00208/) (2020)
- [UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner](https://mlsanity.com/paper/2304.03493/) (2023)
- [A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation](https://mlsanity.com/paper/2307.12574/) (2023)
- [Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets](https://mlsanity.com/paper/2008.07149/) (2020)
- [Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data](https://mlsanity.com/paper/2303.11066/) (2023)
- [FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images](https://mlsanity.com/paper/1907.12056/) (2019)
- [MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization](https://mlsanity.com/paper/2203.14316/) (2022)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training](https://mlsanity.com/paper/2304.14030/) (2023)
- [Universal Segmentation of 33 Anatomies](https://mlsanity.com/paper/2203.02098/) (2022)
- [MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets](https://mlsanity.com/paper/2108.02559/) (2021)
- [Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation](https://mlsanity.com/paper/2303.10049/) (2023)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans](https://mlsanity.com/paper/2407.11529/) (2024)

## Built on by (most cited first, 2 of 2)

- [FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation](https://mlsanity.com/paper/2410.22057/) (2024)
- [HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentation](https://mlsanity.com/paper/2601.04607/) (2026)

## Abstract (arXiv)

The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled datasets. Current methods are inadequate in effectively utilizing the supervised information available from these datasets, thereby impeding the progress in improving the segmentation accuracy. This paper proposes a two-stage multi-organ segmentation method based on mutual learning, aiming to improve multi-organ segmentation performance by complementing information among partially labeled datasets. In the first stage, each partial-organ segmentation model utilizes the non-overlapping organ labels from different datasets and the distinct organ features extracted by different models, introducing additional mutual difference learning to generate higher quality pseudo labels for unlabeled organs. In the second stage, each full-organ segmentation model is supervised by fully labeled datasets with pseudo labels and leverages true labels from other datasets, while dynamically sharing accurate features across different models, introducing additional mutual similarity learning to enhance multi-organ segmentation performance. Extensive experiments were conducted on nine datasets that included the head and neck, chest, abdomen, and pelvis. The results indicate that our method has achieved SOTA performance in segmentation tasks that rely on partial labels, and the ablation studies have thoroughly confirmed the efficacy of the mutual learning mechanism.