# MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets

arXiv: https://arxiv.org/abs/2108.02559 · HTML: https://mlsanity.com/paper/2108.02559/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 13 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Structured Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/1903.04197/) (2019)
- [PseudoSeg: Designing Pseudo Labels for Semantic Segmentation](https://mlsanity.com/paper/2010.09713/) (2020)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)
- [Improving Fast Segmentation With Teacher-student Learning](https://mlsanity.com/paper/1810.08476/) (2018)
- [Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets](https://mlsanity.com/paper/2008.07149/) (2020)
- [Do Deep Convolutional Nets Really Need to be Deep and Convolutional?](https://mlsanity.com/paper/1603.05691/) (2016)

## Built on by (most cited first, 8 of 8)

- [MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation](https://mlsanity.com/paper/2303.14444/) (2023)
- [COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training](https://mlsanity.com/paper/2304.14030/) (2023)
- [Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters](https://mlsanity.com/paper/2406.09679/) (2024)
- [RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction](https://mlsanity.com/paper/2212.10066/) (2022)
- [Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation](https://mlsanity.com/paper/2407.12611/) (2024)
- [Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2409.03228/) (2024)
- [Large Batch and Patch Size Training for Medical Image Segmentation](https://mlsanity.com/paper/2210.13364/) (2022)
- [Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2509.04732/) (2025)

## Abstract (arXiv)

Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper investigates how to learn a multi-organ segmentation model leveraging a set of binary-labeled datasets. A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network. Considering that each teacher focuses on different organs, a region-based supervision method, consisting of logits-wise supervision and feature-wise supervision, is proposed. Each teacher supervises the student in two regions, the organ region where the teacher is considered as an expert and the background region where all teachers agree. Extensive experiments on three public single-organ datasets and a multi-organ dataset have demonstrated the effectiveness of the proposed MS-KD framework.