# Knowledge distillation from multi-modal to mono-modal segmentation networks

arXiv: https://arxiv.org/abs/2106.09564 · HTML: https://mlsanity.com/paper/2106.09564/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

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

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Structured Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/1903.04197/) (2019)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [HeMIS: Hetero-Modal Image Segmentation](https://mlsanity.com/paper/1607.05194/) (2016)
- [Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation](https://mlsanity.com/paper/1907.11150/) (2019)
- [Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion](https://mlsanity.com/paper/2002.09708/) (2020)
- [No New-Net](https://mlsanity.com/paper/1809.10483/) (2018)
- [Improving Fast Segmentation With Teacher-student Learning](https://mlsanity.com/paper/1810.08476/) (2018)
- [Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection](https://mlsanity.com/paper/1802.07846/) (2018)
- [Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs](https://mlsanity.com/paper/1808.06519/) (2018)

## Built on by (most cited first, 30 of 68)

- [Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling](https://mlsanity.com/paper/2307.14126/) (2023)
- [mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation](https://mlsanity.com/paper/2206.02425/) (2022)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2303.05302/) (2023)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2310.01035/) (2023)
- [The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation](https://mlsanity.com/paper/2206.06487/) (2022)
- [ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2106.14591/) (2021)
- [Efficient Medical Image Segmentation Based on Knowledge Distillation](https://mlsanity.com/paper/2108.09987/) (2021)
- [MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning](https://mlsanity.com/paper/2304.08028/) (2023)
- [Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities](https://mlsanity.com/paper/2404.16456/) (2024)
- [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023)
- [Prototype Knowledge Distillation for Medical Segmentation with Missing Modality](https://mlsanity.com/paper/2303.09830/) (2023)
- [SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2507.19264/) (2025)
- [ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities](https://mlsanity.com/paper/2203.04959/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks](https://mlsanity.com/paper/2312.16244/) (2023)
- [COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training](https://mlsanity.com/paper/2304.14030/) (2023)
- [Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning](https://mlsanity.com/paper/2411.02793/) (2024)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [SFusion: Self-attention based N-to-One Multimodal Fusion Block](https://mlsanity.com/paper/2208.12776/) (2022)
- [Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2302.04308/) (2023)
- [Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities](https://mlsanity.com/paper/2111.04735/) (2021)
- [Deep Multimodal Guidance for Medical Image Classification](https://mlsanity.com/paper/2203.05683/) (2022)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Enhanced Multimodal Representation Learning with Cross-modal KD](https://mlsanity.com/paper/2306.07646/) (2023)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation](https://mlsanity.com/paper/2403.11803/) (2024)
- [Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation](https://mlsanity.com/paper/2405.18511/) (2024)
- [MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet](https://mlsanity.com/paper/2206.00902/) (2022)

## Abstract (arXiv)

The joint use of multiple imaging modalities for medical image segmentation has been widely studied in recent years. The fusion of information from different modalities has demonstrated to improve the segmentation accuracy, with respect to mono-modal segmentations, in several applications. However, acquiring multiple modalities is usually not possible in a clinical setting due to a limited number of physicians and scanners, and to limit costs and scan time. Most of the time, only one modality is acquired. In this paper, we propose KD-Net, a framework to transfer knowledge from a trained multi-modal network (teacher) to a mono-modal one (student). The proposed method is an adaptation of the generalized distillation framework where the student network is trained on a subset (1 modality) of the teacher's inputs (n modalities). We illustrate the effectiveness of the proposed framework in brain tumor segmentation with the BraTS 2018 dataset. Using different architectures, we show that the student network effectively learns from the teacher and always outperforms the baseline mono-modal network in terms of segmentation accuracy.